LSTM on DonorsChoose

In [41]:
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import sqlite3
import pandas as pd
import numpy as np
import nltk
import string
import matplotlib.pyplot as plt
import seaborn as sns
from pickle import load,dump
from nltk.stem.porter import PorterStemmer
import re
import string
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer
from sklearn.metrics import roc_auc_score
import pickle
from tqdm import tqdm
import os
import plotly.offline as offline
offline.init_notebook_mode()
from collections import Counter
from sklearn.preprocessing import StandardScaler
from keras.layers import BatchNormalization,Dense,Dropout,Input,Embedding,LSTM,Flatten , GlobalAveragePooling1D, GlobalMaxPooling1D
from keras.layers import Conv1D , MaxPool1D, LeakyReLU, ELU, SpatialDropout1D, MaxPooling1D
from keras.models import Model,Sequential, load_model
from keras.preprocessing.text import Tokenizer, one_hot
from keras.layers.merge import concatenate
from keras.preprocessing.sequence import pad_sequences
from keras import regularizers
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard, ReduceLROnPlateau
from sklearn import model_selection
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
import tensorflow as tf
from sklearn.preprocessing import LabelEncoder

Importing the data

In [14]:
project_data = pd.read_csv('preprocessed_data.csv')
In [15]:
project_data.head(1)
Out[15]:
school_state teacher_prefix project_grade_category teacher_number_of_previously_posted_projects project_is_approved clean_categories clean_subcategories essay price
0 ca mrs grades_prek_2 53 1 math_science appliedsciences health_lifescience i fortunate enough use fairy tale stem kits cl... 725.05

Splitting the data into Test - Train

In [16]:
y=project_data['project_is_approved']
features = project_data.drop(["project_is_approved"],axis=1)
In [17]:
#https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
#split the data into train and test fo bag of words
x_train,x_test,y_train,y_test=model_selection.train_test_split(features,y,test_size=0.30,stratify=y,random_state=0)

Model-1

Essay model

In [19]:
tokenizer = Tokenizer(num_words=5000)
tokenizer.fit_on_texts(x_train["essay"].tolist())
sequences_train = tokenizer.texts_to_sequences(x_train["essay"])
sequences_test = tokenizer.texts_to_sequences(x_test["essay"])
In [20]:
print("No. of datapoints in X_train :",len(x_train))
print("No. of datapoints in X_test :",len(x_test))
print("Shape of Y_train :",y_train.shape)
print("Shape of Y_test :",y_test.shape)

No. of datapoints in X_train : 76473
No. of datapoints in X_test : 32775
Shape of Y_train : (76473,)
Shape of Y_test : (32775,)
In [21]:
# word_index: dictionary mapping words (str) to their rank/index (int)
# http://faroit.com/keras-docs/1.2.2/preprocessing/text/
word2idx = tokenizer.word_index
print('Found %s unique tokens.' % len(word2idx))

Found 49329 unique tokens.
In [22]:
# https://keras.rstudio.com/reference/pad_sequences.html
max_review_length = 350
encoded_train = pad_sequences(sequences_train,maxlen=max_review_length,padding='post')
encoded_test = pad_sequences(sequences_test, maxlen=max_review_length,padding='post')

print('Shape of train data tensor:', encoded_train.shape)
print('Shape of test data tensor:', encoded_test.shape)

Shape of train data tensor: (76473, 350)
Shape of test data tensor: (32775, 350)
In [23]:
# Loading Embedding File
pickle_in = open("glove_vectors","rb")
glove_words = pickle.load(pickle_in)
In [24]:
MAX_VOCAB_SIZE=5000
num_words = min(MAX_VOCAB_SIZE, len(word2idx) + 1)
embedding_matrix = np.zeros((num_words, 300))
for word, i in word2idx.items():
    if i < MAX_VOCAB_SIZE:
    embedding_vector = glove_words.get(word)
    if embedding_vector is not None:
      # words not found in embedding index will be all zeros.
      embedding_matrix[i] = embedding_vector


print(num_words)
print(embedding_matrix.shape)

5000
(5000, 300)
In [26]:
# load pre-trained word embeddings into an Embedding layer
# note that we set trainable = False so as to keep the embeddings fixed
MAX_SEQUENCE_LENGTH=350
embedding_layer = Embedding(
  num_words,
  300,
  weights=[embedding_matrix],
  input_length=MAX_SEQUENCE_LENGTH,
  trainable=False
)
input_text = Input(shape=(MAX_SEQUENCE_LENGTH,),name="input_text")
x = embedding_layer(input_text)
x = LSTM(4,return_sequences=True)(x) # dropout=0.5
flatten_1 = Flatten()(x)

Teacher prefix layer

In [28]:
no_of_unique_prefix  = x_train["teacher_prefix"].nunique()
embedding_size_prefix = int(min(np.ceil((no_of_unique_prefix)/2), 50 ))
# Input and Embedding Layer 
input_prefix = Input(shape=(1,),name="teacher_prefix")
embedding_prefix = Embedding(no_of_unique_prefix,embedding_size_prefix,name="emb_pre",trainable=True)(input_prefix)
flatten_2 = Flatten()(embedding_prefix)

lb = LabelEncoder()
encoder_prefix_train = lb.fit_transform(x_train["teacher_prefix"])
encoder_prefix_test = lb.transform(x_test["teacher_prefix"])

School State layer

In [29]:
no_of_unique_state  = x_train["school_state"].nunique()
embedding_size_state= int(min(np.ceil((no_of_unique_state)/2), 50 ))
# Input and Embedding Layer 
input_state = Input(shape=(1,),name="school_prefix")
embedding_state = Embedding(no_of_unique_state,embedding_size_state,name="emb_state",trainable=True)(input_state)
flatten_3 = Flatten()(embedding_state)

encoder_state_train = lb.fit_transform(x_train["school_state"])
encoder_state_test = lb.transform(x_test["school_state"])

Project Grade Category layer

In [30]:
no_of_unique_grade  = x_train["project_grade_category"].nunique()
embedding_size_grade = int(min(np.ceil((no_of_unique_grade)/2), 50 ))
# Input and Embedding Layer 
input_grade= Input(shape=(1,),name="grade_cat")
embedding_grade = Embedding(no_of_unique_grade,embedding_size_grade,name="emb_grade",trainable=True)(input_grade)
flatten_4 = Flatten()(embedding_grade)

encoder_grade_train = lb.fit_transform(x_train["project_grade_category"])
encoder_grade_test = lb.transform(x_test["project_grade_category"])

Clean Categories layer

In [31]:
no_of_unique_subcat  = x_train["clean_categories"].nunique()
embedding_size_subcat = int(min(np.ceil((no_of_unique_subcat)/2), 50 ))

# Input and Embedding Layer
input_subcat= Input(shape=(1,),name="sub_cat")
embedding_subcat = Embedding(no_of_unique_subcat,embedding_size_subcat,name="emb_subcat",trainable=True)(input_subcat)
flatten_5 = Flatten()(embedding_subcat)

le = LabelEncoder()
le.fit(x_train["clean_categories"])
x_test["clean_categories"] = x_test["clean_categories"].map(lambda s: '<unknown>' if s not in le.classes_ else s) #unknown is used as value if are any unknown values
le.classes_ = np.append(le.classes_, '<unknown>')
encoder_subcat_train = le.transform(x_train["clean_categories"])
encoder_subcat_test= le.transform(x_test["clean_categories"])

Clean subcategories layer

In [32]:
no_of_unique_subcat_1  = x_train["clean_subcategories"].nunique()
embedding_size_subcat_1 = int(min(np.ceil((no_of_unique_subcat_1)/2), 50 ))
# Input and Embedding Layer 
input_subcat_1= Input(shape=(1,),name="sub_cat_1")
embedding_subcat_1 = Embedding(no_of_unique_subcat_1+1,embedding_size_subcat_1,name="emb_subcat_1",trainable=True)(input_subcat_1)#adding +1
flatten_6 = Flatten()(embedding_subcat_1)
le = LabelEncoder()
le.fit(x_train["clean_subcategories"])
x_test["clean_subcategories"] = x_test["clean_subcategories"].map(lambda s: '<unknown>' if s not in le.classes_ else s)
le.classes_ = np.append(le.classes_, '<unknown>')

encoder_subcat_1_train = le.transform(x_train["clean_subcategories"])
encoder_subcat_1_test= le.transform(x_test["clean_subcategories"])

Numerical features

In [33]:
# Preparing Text Data As per Our Model
x_train["len_essay"] = x_train["essay"].apply(len)
x_test["len_essay"] = x_test["essay"].apply(len)
In [34]:
num_train_1=x_train['len_essay'].values.reshape(-1, 1)
num_train_2=x_train['price'].values.reshape(-1, 1)
num_train_3=x_train['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1)

num_test_1=x_test['len_essay'].values.reshape(-1, 1)
num_test_2=x_test['price'].values.reshape(-1, 1)
num_test_3=x_test['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1)
num_train=np.concatenate((num_train_1,num_train_2,num_train_3),axis=1)
num_test=np.concatenate((num_test_1,num_test_2,num_test_3),axis=1)
In [35]:
from sklearn.preprocessing import StandardScaler
norm=StandardScaler()
norm_train=norm.fit_transform(num_train)
norm_test=norm.transform(num_test)
In [36]:
# Input and Embedding Layer 
num_feats = Input(shape=(3,),name="numerical_features")
num_feats_ = Dense(1,activation="relu")(num_feats)
In [37]:
x_concatenate = concatenate([flatten_1,flatten_2,flatten_3,flatten_4,flatten_5,flatten_6,num_feats_])
In [38]:
x = Dense(16,activation="relu")(x_concatenate)
x=Dropout(0.2)(x)
x = Dense(8,activation="relu")(x)
x=Dropout(0.2)(x)
x = Dense(4,activation="relu")(x)
x = BatchNormalization()(x)
output = Dense(2, activation='sigmoid', name='output')(x)
model_1 = Model(inputs=[input_text,input_prefix,input_state,input_grade,
                        input_subcat,input_subcat_1,num_feats],outputs=[output])
In [39]:
train_data_1 = [encoded_train,encoder_prefix_train,encoder_state_train,
              encoder_grade_train,encoder_subcat_train,encoder_subcat_1_train,norm_train]
test_data_1 = [encoded_test,encoder_prefix_test,encoder_state_test,encoder_grade_test,
             encoder_subcat_test,encoder_subcat_1_test,norm_test]
from keras.utils import np_utils
Y_train = np_utils.to_categorical(y_train, 2) 
Y_test = np_utils.to_categorical(y_test, 2)
In [41]:
checkpoint_1 = ModelCheckpoint("model_1.h5", monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
earlystop_1 = EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=1, mode='auto', baseline=None, restore_best_weights=True)
reduce_lr_1 = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.2, patience = 1, verbose = 1, min_delta = 0.0001)
tensorboard_1 = TensorBoard(log_dir='graph_1', histogram_freq=0, batch_size=64, write_graph=True, write_grads=False, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None, embeddings_data=None, update_freq='epoch')
callbacks_1 = [checkpoint_1,earlystop_1,tensorboard_1,reduce_lr_1]
In [43]:
# Defining Custom ROC-AUC Metrics
from sklearn.metrics import roc_auc_score

def auc1(y_true, y_pred):
    if len(np.unique(y_true[:,1])) == 1:
        return 0.5
    else:
        return roc_auc_score(y_true, y_pred)

def auroc(y_true, y_pred):
    return tf.py_func(auc1, (y_true, y_pred), tf.double)
In [42]:
# model compilation
model_1.compile(loss='binary_crossentropy', optimizer='adam',metrics=[auroc])
In [45]:
model_1.summary()

Model: "model_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_text (InputLayer)         (None, 350)          0                                            
__________________________________________________________________________________________________
embedding_1 (Embedding)         (None, 350, 300)     1500000     input_text[0][0]                 
__________________________________________________________________________________________________
teacher_prefix (InputLayer)     (None, 1)            0                                            
__________________________________________________________________________________________________
school_prefix (InputLayer)      (None, 1)            0                                            
__________________________________________________________________________________________________
grade_cat (InputLayer)          (None, 1)            0                                            
__________________________________________________________________________________________________
sub_cat (InputLayer)            (None, 1)            0                                            
__________________________________________________________________________________________________
sub_cat_1 (InputLayer)          (None, 1)            0                                            
__________________________________________________________________________________________________
lstm_1 (LSTM)                   (None, 350, 4)       4880        embedding_1[0][0]                
__________________________________________________________________________________________________
emb_pre (Embedding)             (None, 1, 3)         15          teacher_prefix[0][0]             
__________________________________________________________________________________________________
emb_state (Embedding)           (None, 1, 26)        1326        school_prefix[0][0]              
__________________________________________________________________________________________________
emb_grade (Embedding)           (None, 1, 2)         8           grade_cat[0][0]                  
__________________________________________________________________________________________________
emb_subcat (Embedding)          (None, 1, 26)        1326        sub_cat[0][0]                    
__________________________________________________________________________________________________
emb_subcat_1 (Embedding)        (None, 1, 50)        19650       sub_cat_1[0][0]                  
__________________________________________________________________________________________________
numerical_features (InputLayer) (None, 3)            0                                            
__________________________________________________________________________________________________
flatten_1 (Flatten)             (None, 1400)         0           lstm_1[0][0]                     
__________________________________________________________________________________________________
flatten_2 (Flatten)             (None, 3)            0           emb_pre[0][0]                    
__________________________________________________________________________________________________
flatten_3 (Flatten)             (None, 26)           0           emb_state[0][0]                  
__________________________________________________________________________________________________
flatten_4 (Flatten)             (None, 2)            0           emb_grade[0][0]                  
__________________________________________________________________________________________________
flatten_5 (Flatten)             (None, 26)           0           emb_subcat[0][0]                 
__________________________________________________________________________________________________
flatten_6 (Flatten)             (None, 50)           0           emb_subcat_1[0][0]               
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 1)            4           numerical_features[0][0]         
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 1508)         0           flatten_1[0][0]                  
                                                                 flatten_2[0][0]                  
                                                                 flatten_3[0][0]                  
                                                                 flatten_4[0][0]                  
                                                                 flatten_5[0][0]                  
                                                                 flatten_6[0][0]                  
                                                                 dense_1[0][0]                    
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 16)           24144       concatenate_1[0][0]              
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 16)           0           dense_2[0][0]                    
__________________________________________________________________________________________________
dense_3 (Dense)                 (None, 8)            136         dropout_1[0][0]                  
__________________________________________________________________________________________________
dropout_2 (Dropout)             (None, 8)            0           dense_3[0][0]                    
__________________________________________________________________________________________________
dense_4 (Dense)                 (None, 4)            36          dropout_2[0][0]                  
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 4)            16          dense_4[0][0]                    
__________________________________________________________________________________________________
output (Dense)                  (None, 2)            10          batch_normalization_1[0][0]      
==================================================================================================
Total params: 1,551,551
Trainable params: 51,543
Non-trainable params: 1,500,008
__________________________________________________________________________________________________
In [193]:
history_1 = model_1.fit(train_data_1,Y_train,batch_size=64,
                        epochs=20,validation_data=(test_data_1,Y_test),callbacks=callbacks_1)

Train on 76473 samples, validate on 32775 samples
Epoch 1/20
12352/76473 [===>..........................] - ETA: 1:03:57 - loss: 0.8033 - auroc: 0.42 - ETA: 44:42 - loss: 0.7878 - auroc: 0.3891 - ETA: 38:37 - loss: 0.7718 - auroc: 0.44 - ETA: 34:58 - loss: 0.7719 - auroc: 0.46 - ETA: 33:24 - loss: 0.7614 - auroc: 0.48 - ETA: 31:59 - loss: 0.7613 - auroc: 0.49 - ETA: 31:08 - loss: 0.7534 - auroc: 0.51 - ETA: 30:24 - loss: 0.7491 - auroc: 0.50 - ETA: 29:43 - loss: 0.7466 - auroc: 0.50 - ETA: 29:07 - loss: 0.7458 - auroc: 0.48 - ETA: 28:35 - loss: 0.7477 - auroc: 0.46 - ETA: 28:15 - loss: 0.7494 - auroc: 0.46 - ETA: 27:59 - loss: 0.7449 - auroc: 0.47 - ETA: 27:42 - loss: 0.7416 - auroc: 0.48 - ETA: 27:31 - loss: 0.7406 - auroc: 0.48 - ETA: 27:23 - loss: 0.7384 - auroc: 0.48 - ETA: 27:13 - loss: 0.7341 - auroc: 0.49 - ETA: 27:08 - loss: 0.7309 - auroc: 0.50 - ETA: 26:56 - loss: 0.7291 - auroc: 0.50 - ETA: 26:45 - loss: 0.7273 - auroc: 0.50 - ETA: 26:36 - loss: 0.7257 - auroc: 0.50 - ETA: 26:27 - loss: 0.7252 - auroc: 0.50 - ETA: 26:21 - loss: 0.7243 - 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auroc: 0.52 - ETA: 29s - loss: 0.4640 - auroc: 0.52 - ETA: 28s - loss: 0.4639 - auroc: 0.52 - ETA: 27s - loss: 0.4639 - auroc: 0.52 - ETA: 25s - loss: 0.4639 - auroc: 0.52 - ETA: 24s - loss: 0.4639 - auroc: 0.52 - ETA: 23s - loss: 0.4639 - auroc: 0.52 - ETA: 22s - loss: 0.4638 - auroc: 0.52 - ETA: 20s - loss: 0.4639 - auroc: 0.52 - ETA: 19s - loss: 0.4639 - auroc: 0.52 - ETA: 18s - loss: 0.4639 - auroc: 0.52 - ETA: 17s - loss: 0.4638 - auroc: 0.52 - ETA: 15s - loss: 0.4638 - auroc: 0.52 - ETA: 14s - loss: 0.4638 - auroc: 0.52 - ETA: 13s - loss: 0.4638 - auroc: 0.52 - ETA: 12s - loss: 0.4638 - auroc: 0.52 - ETA: 11s - loss: 0.4638 - auroc: 0.52 - ETA: 9s - loss: 0.4637 - auroc: 0.5282 - ETA: 8s - loss: 0.4637 - auroc: 0.528 - ETA: 7s - loss: 0.4636 - auroc: 0.528 - ETA: 6s - loss: 0.4636 - auroc: 0.528 - ETA: 4s - loss: 0.4635 - auroc: 0.528 - ETA: 3s - loss: 0.4635 - auroc: 0.528 - ETA: 2s - loss: 0.4635 - auroc: 0.528 - ETA: 1s - loss: 0.4635 - auroc: 0.528 - 1993s 26ms/step - loss: 0.4635 - auroc: 0.5284 - val_loss: 0.4080 - val_auroc: 0.6781

Epoch 00001: val_loss improved from inf to 0.40803, saving model to model_1.h5
Epoch 2/20
12416/76473 [===>..........................] - ETA: 24:02 - loss: 0.4564 - auroc: 0.53 - ETA: 24:49 - loss: 0.3458 - auroc: 0.63 - ETA: 24:29 - loss: 0.3653 - auroc: 0.65 - ETA: 24:18 - loss: 0.3631 - auroc: 0.66 - ETA: 24:24 - loss: 0.3611 - auroc: 0.68 - ETA: 24:25 - loss: 0.3687 - auroc: 0.68 - ETA: 24:28 - loss: 0.3927 - auroc: 0.65 - ETA: 24:25 - loss: 0.3853 - auroc: 0.66 - ETA: 24:25 - loss: 0.3718 - auroc: 0.66 - ETA: 24:26 - loss: 0.3744 - auroc: 0.68 - ETA: 24:21 - loss: 0.3782 - auroc: 0.69 - ETA: 24:17 - loss: 0.3847 - auroc: 0.69 - ETA: 24:14 - loss: 0.3925 - auroc: 0.68 - ETA: 24:12 - loss: 0.3880 - auroc: 0.66 - ETA: 24:12 - loss: 0.3896 - auroc: 0.66 - ETA: 24:11 - loss: 0.3903 - auroc: 0.67 - ETA: 24:07 - loss: 0.3878 - auroc: 0.67 - ETA: 24:06 - loss: 0.3869 - auroc: 0.67 - ETA: 24:05 - loss: 0.3860 - auroc: 0.67 - ETA: 24:04 - loss: 0.3881 - auroc: 0.67 - ETA: 24:03 - loss: 0.3949 - auroc: 0.68 - ETA: 24:03 - loss: 0.3963 - auroc: 0.68 - ETA: 24:01 - loss: 0.4022 - auroc: 0.68 - 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ETA: 58s - loss: 0.3943 - auroc: 0.70 - ETA: 57s - loss: 0.3943 - auroc: 0.70 - ETA: 56s - loss: 0.3944 - auroc: 0.70 - ETA: 54s - loss: 0.3943 - auroc: 0.70 - ETA: 53s - loss: 0.3942 - auroc: 0.70 - ETA: 52s - loss: 0.3942 - auroc: 0.70 - ETA: 51s - loss: 0.3942 - auroc: 0.70 - ETA: 49s - loss: 0.3942 - auroc: 0.70 - ETA: 48s - loss: 0.3941 - auroc: 0.70 - ETA: 47s - loss: 0.3940 - auroc: 0.70 - ETA: 46s - loss: 0.3941 - auroc: 0.70 - ETA: 45s - loss: 0.3941 - auroc: 0.70 - ETA: 43s - loss: 0.3942 - auroc: 0.70 - ETA: 42s - loss: 0.3942 - auroc: 0.70 - ETA: 41s - loss: 0.3942 - auroc: 0.70 - ETA: 40s - loss: 0.3941 - auroc: 0.70 - ETA: 38s - loss: 0.3942 - auroc: 0.70 - ETA: 37s - loss: 0.3942 - auroc: 0.70 - ETA: 36s - loss: 0.3943 - auroc: 0.706776473/76473 [==============================] - ETA: 35s - loss: 0.3943 - auroc: 0.70 - ETA: 34s - loss: 0.3942 - auroc: 0.70 - ETA: 32s - loss: 0.3941 - auroc: 0.70 - ETA: 31s - loss: 0.3943 - auroc: 0.70 - ETA: 30s - loss: 0.3942 - auroc: 0.70 - ETA: 29s - loss: 0.3943 - auroc: 0.70 - ETA: 27s - loss: 0.3943 - auroc: 0.70 - ETA: 26s - loss: 0.3943 - auroc: 0.70 - ETA: 25s - loss: 0.3943 - auroc: 0.70 - ETA: 24s - loss: 0.3943 - auroc: 0.70 - ETA: 23s - loss: 0.3943 - auroc: 0.70 - ETA: 21s - loss: 0.3945 - auroc: 0.70 - ETA: 20s - loss: 0.3945 - auroc: 0.70 - ETA: 19s - loss: 0.3945 - auroc: 0.70 - ETA: 18s - loss: 0.3946 - auroc: 0.70 - ETA: 16s - loss: 0.3946 - auroc: 0.70 - ETA: 15s - loss: 0.3945 - auroc: 0.70 - ETA: 14s - loss: 0.3945 - auroc: 0.70 - ETA: 13s - loss: 0.3944 - auroc: 0.70 - ETA: 12s - loss: 0.3943 - auroc: 0.70 - ETA: 10s - loss: 0.3942 - auroc: 0.70 - ETA: 9s - loss: 0.3941 - auroc: 0.7065 - ETA: 8s - loss: 0.3941 - auroc: 0.706 - ETA: 7s - loss: 0.3940 - auroc: 0.706 - ETA: 5s - loss: 0.3939 - auroc: 0.706 - ETA: 4s - loss: 0.3940 - auroc: 0.706 - ETA: 3s - loss: 0.3939 - auroc: 0.706 - ETA: 2s - loss: 0.3939 - auroc: 0.706 - ETA: 1s - loss: 0.3940 - auroc: 0.706 - 1963s 26ms/step - loss: 0.3940 - auroc: 0.7066 - val_loss: 0.3856 - val_auroc: 0.7414

Epoch 00002: val_loss improved from 0.40803 to 0.38558, saving model to model_1.h5
Epoch 3/20
12416/76473 [===>..........................] - ETA: 24:00 - loss: 0.2767 - auroc: 0.82 - ETA: 24:10 - loss: 0.3189 - auroc: 0.74 - ETA: 24:07 - loss: 0.3190 - auroc: 0.74 - ETA: 24:16 - loss: 0.3659 - auroc: 0.73 - ETA: 24:14 - loss: 0.3905 - auroc: 0.72 - ETA: 24:52 - loss: 0.3953 - auroc: 0.71 - ETA: 24:46 - loss: 0.3880 - auroc: 0.72 - ETA: 24:40 - loss: 0.3773 - auroc: 0.73 - ETA: 24:45 - loss: 0.3680 - auroc: 0.74 - ETA: 24:44 - loss: 0.3650 - auroc: 0.76 - ETA: 24:35 - loss: 0.3623 - auroc: 0.76 - ETA: 24:31 - loss: 0.3692 - auroc: 0.77 - ETA: 24:30 - loss: 0.3717 - auroc: 0.78 - ETA: 24:26 - loss: 0.3694 - auroc: 0.78 - ETA: 24:25 - loss: 0.3678 - auroc: 0.76 - ETA: 24:24 - loss: 0.3650 - auroc: 0.76 - ETA: 24:21 - loss: 0.3718 - auroc: 0.75 - ETA: 24:21 - loss: 0.3721 - auroc: 0.75 - ETA: 24:19 - loss: 0.3757 - auroc: 0.75 - ETA: 24:15 - loss: 0.3820 - auroc: 0.75 - ETA: 24:13 - loss: 0.3800 - auroc: 0.74 - ETA: 24:11 - loss: 0.3831 - auroc: 0.74 - ETA: 24:09 - loss: 0.3810 - auroc: 0.74 - ETA: 24:06 - loss: 0.3805 - auroc: 0.74 - ETA: 24:03 - loss: 0.3821 - auroc: 0.74 - ETA: 24:02 - loss: 0.3778 - auroc: 0.75 - ETA: 24:01 - loss: 0.3805 - auroc: 0.75 - ETA: 23:59 - loss: 0.3808 - auroc: 0.75 - ETA: 23:58 - loss: 0.3794 - auroc: 0.75 - ETA: 23:56 - loss: 0.3813 - auroc: 0.75 - ETA: 23:53 - loss: 0.3811 - auroc: 0.74 - ETA: 23:52 - loss: 0.3786 - auroc: 0.74 - ETA: 23:50 - loss: 0.3777 - auroc: 0.74 - ETA: 23:49 - loss: 0.3751 - auroc: 0.75 - ETA: 23:48 - loss: 0.3764 - auroc: 0.75 - ETA: 23:45 - loss: 0.3777 - auroc: 0.75 - ETA: 23:43 - loss: 0.3825 - auroc: 0.75 - ETA: 23:41 - loss: 0.3823 - auroc: 0.75 - ETA: 23:41 - loss: 0.3813 - auroc: 0.75 - ETA: 23:40 - loss: 0.3833 - auroc: 0.75 - ETA: 23:38 - loss: 0.3816 - auroc: 0.75 - ETA: 23:37 - loss: 0.3828 - auroc: 0.75 - ETA: 23:36 - loss: 0.3846 - auroc: 0.75 - ETA: 23:34 - loss: 0.3851 - auroc: 0.75 - ETA: 23:33 - loss: 0.3857 - auroc: 0.75 - ETA: 23:32 - loss: 0.3871 - auroc: 0.74 - ETA: 23:30 - loss: 0.3859 - auroc: 0.75 - ETA: 23:29 - loss: 0.3844 - auroc: 0.75 - ETA: 23:27 - loss: 0.3850 - auroc: 0.75 - ETA: 23:25 - loss: 0.3861 - auroc: 0.74 - ETA: 23:24 - loss: 0.3885 - auroc: 0.74 - ETA: 23:23 - loss: 0.3871 - auroc: 0.74 - ETA: 23:22 - loss: 0.3873 - auroc: 0.74 - ETA: 23:21 - loss: 0.3880 - auroc: 0.74 - ETA: 23:20 - loss: 0.3886 - auroc: 0.74 - ETA: 23:18 - loss: 0.3890 - auroc: 0.74 - ETA: 23:17 - loss: 0.3866 - auroc: 0.74 - ETA: 23:15 - loss: 0.3887 - auroc: 0.74 - ETA: 23:14 - loss: 0.3894 - auroc: 0.74 - ETA: 23:12 - loss: 0.3883 - auroc: 0.74 - ETA: 23:11 - loss: 0.3888 - auroc: 0.74 - ETA: 23:10 - loss: 0.3876 - auroc: 0.74 - ETA: 23:08 - loss: 0.3887 - auroc: 0.74 - ETA: 23:07 - loss: 0.3876 - auroc: 0.74 - ETA: 23:05 - loss: 0.3878 - auroc: 0.74 - ETA: 23:04 - loss: 0.3864 - auroc: 0.74 - ETA: 23:02 - loss: 0.3852 - auroc: 0.74 - ETA: 23:01 - loss: 0.3844 - auroc: 0.74 - ETA: 23:01 - loss: 0.3858 - auroc: 0.74 - ETA: 22:59 - loss: 0.3845 - auroc: 0.74 - ETA: 22:58 - loss: 0.3855 - auroc: 0.74 - ETA: 22:57 - loss: 0.3857 - auroc: 0.74 - ETA: 22:55 - loss: 0.3861 - auroc: 0.74 - ETA: 22:54 - loss: 0.3873 - auroc: 0.74 - ETA: 22:53 - loss: 0.3880 - auroc: 0.74 - ETA: 22:52 - loss: 0.3868 - auroc: 0.74 - ETA: 22:51 - loss: 0.3869 - auroc: 0.74 - ETA: 22:49 - loss: 0.3863 - auroc: 0.74 - ETA: 22:48 - loss: 0.3857 - auroc: 0.74 - ETA: 22:46 - loss: 0.3888 - auroc: 0.74 - ETA: 22:45 - loss: 0.3917 - auroc: 0.74 - ETA: 22:45 - loss: 0.3913 - auroc: 0.73 - ETA: 22:44 - loss: 0.3899 - auroc: 0.73 - ETA: 22:43 - loss: 0.3885 - auroc: 0.74 - ETA: 22:42 - loss: 0.3885 - auroc: 0.74 - ETA: 22:41 - loss: 0.3879 - auroc: 0.74 - ETA: 22:40 - loss: 0.3864 - auroc: 0.74 - ETA: 22:39 - loss: 0.3852 - auroc: 0.74 - ETA: 22:37 - loss: 0.3855 - auroc: 0.74 - ETA: 22:36 - loss: 0.3846 - auroc: 0.74 - ETA: 22:34 - loss: 0.3850 - auroc: 0.74 - ETA: 22:33 - loss: 0.3848 - auroc: 0.74 - ETA: 22:32 - loss: 0.3839 - auroc: 0.74 - ETA: 22:31 - loss: 0.3831 - auroc: 0.74 - ETA: 22:30 - loss: 0.3827 - auroc: 0.75 - ETA: 22:28 - loss: 0.3821 - auroc: 0.75 - ETA: 22:27 - loss: 0.3817 - auroc: 0.75 - ETA: 22:26 - loss: 0.3818 - auroc: 0.75 - ETA: 22:25 - loss: 0.3818 - auroc: 0.75 - ETA: 22:24 - loss: 0.3814 - auroc: 0.74 - ETA: 22:22 - loss: 0.3814 - auroc: 0.74 - ETA: 22:21 - loss: 0.3806 - auroc: 0.74 - ETA: 22:19 - loss: 0.3813 - auroc: 0.74 - ETA: 22:19 - loss: 0.3811 - auroc: 0.74 - ETA: 22:18 - loss: 0.3813 - auroc: 0.74 - ETA: 22:17 - loss: 0.3815 - auroc: 0.74 - ETA: 22:16 - loss: 0.3811 - auroc: 0.74 - ETA: 22:14 - loss: 0.3812 - auroc: 0.74 - ETA: 22:13 - loss: 0.3806 - auroc: 0.74 - ETA: 22:11 - loss: 0.3807 - auroc: 0.74 - ETA: 22:10 - loss: 0.3813 - auroc: 0.74 - ETA: 22:09 - loss: 0.3810 - auroc: 0.74 - ETA: 22:08 - loss: 0.3811 - auroc: 0.74 - ETA: 22:06 - loss: 0.3805 - auroc: 0.74 - ETA: 22:05 - loss: 0.3811 - auroc: 0.74 - ETA: 22:04 - loss: 0.3803 - auroc: 0.74 - ETA: 22:03 - loss: 0.3810 - auroc: 0.74 - ETA: 22:01 - loss: 0.3802 - auroc: 0.74 - ETA: 22:00 - loss: 0.3803 - auroc: 0.74 - ETA: 21:59 - loss: 0.3804 - auroc: 0.74 - ETA: 21:57 - loss: 0.3795 - auroc: 0.74 - ETA: 21:56 - loss: 0.3797 - auroc: 0.74 - ETA: 21:55 - loss: 0.3792 - auroc: 0.74 - ETA: 21:54 - loss: 0.3801 - auroc: 0.74 - ETA: 21:53 - loss: 0.3794 - auroc: 0.74 - ETA: 21:51 - loss: 0.3799 - auroc: 0.74 - ETA: 21:50 - loss: 0.3796 - auroc: 0.74 - ETA: 21:49 - loss: 0.3790 - auroc: 0.74 - ETA: 21:48 - loss: 0.3795 - auroc: 0.74 - ETA: 21:47 - loss: 0.3794 - auroc: 0.74 - ETA: 21:45 - loss: 0.3800 - auroc: 0.74 - ETA: 21:44 - loss: 0.3804 - auroc: 0.74 - ETA: 21:43 - loss: 0.3798 - auroc: 0.74 - ETA: 21:41 - loss: 0.3791 - auroc: 0.74 - ETA: 21:40 - loss: 0.3795 - auroc: 0.74 - ETA: 21:39 - loss: 0.3793 - auroc: 0.74 - ETA: 21:38 - loss: 0.3795 - auroc: 0.74 - ETA: 21:36 - loss: 0.3802 - auroc: 0.74 - ETA: 21:35 - loss: 0.3801 - auroc: 0.74 - ETA: 21:34 - loss: 0.3802 - auroc: 0.74 - ETA: 21:33 - loss: 0.3794 - auroc: 0.74 - ETA: 21:32 - loss: 0.3789 - auroc: 0.74 - ETA: 21:31 - loss: 0.3798 - auroc: 0.74 - ETA: 21:29 - loss: 0.3793 - auroc: 0.74 - ETA: 21:28 - loss: 0.3798 - auroc: 0.74 - ETA: 21:27 - loss: 0.3800 - auroc: 0.74 - ETA: 21:26 - loss: 0.3807 - auroc: 0.74 - ETA: 21:25 - loss: 0.3810 - auroc: 0.74 - ETA: 21:23 - loss: 0.3812 - auroc: 0.74 - ETA: 21:22 - loss: 0.3807 - auroc: 0.74 - ETA: 21:21 - loss: 0.3801 - auroc: 0.74 - ETA: 21:20 - loss: 0.3798 - auroc: 0.74 - ETA: 21:19 - loss: 0.3798 - auroc: 0.74 - ETA: 21:17 - loss: 0.3794 - auroc: 0.74 - ETA: 21:16 - loss: 0.3787 - auroc: 0.74 - ETA: 21:14 - loss: 0.3787 - auroc: 0.74 - ETA: 21:13 - loss: 0.3785 - auroc: 0.74 - ETA: 21:12 - loss: 0.3791 - auroc: 0.74 - ETA: 21:11 - loss: 0.3790 - auroc: 0.74 - ETA: 21:10 - loss: 0.3789 - auroc: 0.74 - ETA: 21:08 - loss: 0.3787 - auroc: 0.74 - ETA: 21:07 - loss: 0.3796 - auroc: 0.74 - ETA: 21:06 - loss: 0.3793 - auroc: 0.74 - ETA: 21:05 - loss: 0.3790 - auroc: 0.74 - ETA: 21:04 - loss: 0.3792 - auroc: 0.74 - ETA: 21:02 - loss: 0.3798 - auroc: 0.74 - ETA: 21:01 - loss: 0.3796 - auroc: 0.74 - 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ETA: 59s - loss: 0.3796 - auroc: 0.740 - ETA: 58s - loss: 0.3796 - auroc: 0.74 - ETA: 56s - loss: 0.3796 - auroc: 0.74 - ETA: 55s - loss: 0.3797 - auroc: 0.74 - ETA: 54s - loss: 0.3798 - auroc: 0.74 - ETA: 53s - loss: 0.3799 - auroc: 0.74 - ETA: 51s - loss: 0.3798 - auroc: 0.74 - ETA: 50s - loss: 0.3800 - auroc: 0.74 - ETA: 49s - loss: 0.3799 - auroc: 0.74 - ETA: 48s - loss: 0.3801 - auroc: 0.74 - ETA: 46s - loss: 0.3800 - auroc: 0.74 - ETA: 45s - loss: 0.3800 - auroc: 0.74 - ETA: 44s - loss: 0.3800 - auroc: 0.74 - ETA: 43s - loss: 0.3801 - auroc: 0.74 - ETA: 41s - loss: 0.3800 - auroc: 0.74 - ETA: 40s - loss: 0.3802 - auroc: 0.74 - ETA: 39s - loss: 0.3801 - auroc: 0.74 - ETA: 38s - loss: 0.3801 - auroc: 0.74 - ETA: 36s - loss: 0.3800 - auroc: 0.740776473/76473 [==============================] - ETA: 35s - loss: 0.3799 - auroc: 0.74 - ETA: 34s - loss: 0.3798 - auroc: 0.74 - ETA: 33s - loss: 0.3798 - auroc: 0.74 - ETA: 32s - loss: 0.3799 - auroc: 0.74 - ETA: 30s - loss: 0.3798 - auroc: 0.74 - ETA: 29s - loss: 0.3799 - auroc: 0.74 - ETA: 28s - loss: 0.3800 - auroc: 0.74 - ETA: 27s - loss: 0.3799 - auroc: 0.74 - ETA: 25s - loss: 0.3800 - auroc: 0.74 - ETA: 24s - loss: 0.3801 - auroc: 0.74 - ETA: 23s - loss: 0.3802 - auroc: 0.74 - ETA: 22s - loss: 0.3802 - auroc: 0.74 - ETA: 20s - loss: 0.3802 - auroc: 0.74 - ETA: 19s - loss: 0.3802 - auroc: 0.74 - ETA: 18s - loss: 0.3801 - auroc: 0.74 - ETA: 17s - loss: 0.3800 - auroc: 0.74 - ETA: 15s - loss: 0.3799 - auroc: 0.74 - ETA: 14s - loss: 0.3799 - auroc: 0.74 - ETA: 13s - loss: 0.3799 - auroc: 0.74 - ETA: 12s - loss: 0.3798 - auroc: 0.74 - ETA: 10s - loss: 0.3799 - auroc: 0.74 - ETA: 9s - loss: 0.3799 - auroc: 0.7411 - ETA: 8s - loss: 0.3798 - auroc: 0.741 - ETA: 7s - loss: 0.3798 - auroc: 0.741 - ETA: 6s - loss: 0.3799 - auroc: 0.741 - ETA: 4s - loss: 0.3799 - auroc: 0.741 - ETA: 3s - loss: 0.3799 - auroc: 0.741 - ETA: 2s - loss: 0.3800 - auroc: 0.741 - ETA: 1s - loss: 0.3800 - auroc: 0.741 - 1976s 26ms/step - loss: 0.3799 - auroc: 0.7412 - val_loss: 0.3776 - val_auroc: 0.7489

Epoch 00003: val_loss improved from 0.38558 to 0.37759, saving model to model_1.h5
Epoch 4/20
12416/76473 [===>..........................] - ETA: 24:55 - loss: 0.4428 - auroc: 0.67 - ETA: 24:43 - loss: 0.4312 - auroc: 0.66 - ETA: 24:39 - loss: 0.3914 - auroc: 0.72 - ETA: 24:40 - loss: 0.3683 - auroc: 0.73 - ETA: 24:44 - loss: 0.3724 - auroc: 0.76 - ETA: 24:37 - loss: 0.3533 - auroc: 0.79 - ETA: 24:39 - loss: 0.3643 - auroc: 0.79 - ETA: 24:40 - loss: 0.3445 - auroc: 0.80 - ETA: 24:42 - loss: 0.3557 - auroc: 0.80 - ETA: 24:33 - loss: 0.3585 - auroc: 0.80 - ETA: 24:34 - loss: 0.3510 - auroc: 0.80 - ETA: 24:35 - loss: 0.3529 - auroc: 0.80 - ETA: 24:31 - loss: 0.3627 - auroc: 0.79 - ETA: 24:29 - loss: 0.3630 - auroc: 0.80 - ETA: 24:26 - loss: 0.3636 - auroc: 0.80 - ETA: 24:24 - loss: 0.3603 - auroc: 0.81 - ETA: 24:21 - loss: 0.3635 - auroc: 0.80 - ETA: 24:17 - loss: 0.3666 - auroc: 0.80 - ETA: 24:12 - loss: 0.3628 - auroc: 0.80 - ETA: 24:11 - loss: 0.3625 - auroc: 0.80 - ETA: 24:08 - loss: 0.3591 - auroc: 0.80 - ETA: 24:07 - loss: 0.3613 - auroc: 0.80 - ETA: 24:06 - loss: 0.3674 - auroc: 0.80 - ETA: 24:05 - loss: 0.3667 - auroc: 0.80 - ETA: 24:02 - loss: 0.3673 - auroc: 0.80 - ETA: 24:00 - loss: 0.3620 - auroc: 0.80 - ETA: 23:59 - loss: 0.3628 - auroc: 0.79 - ETA: 23:58 - loss: 0.3613 - auroc: 0.79 - ETA: 23:57 - loss: 0.3577 - auroc: 0.79 - ETA: 23:55 - loss: 0.3624 - auroc: 0.79 - ETA: 23:53 - loss: 0.3661 - auroc: 0.78 - ETA: 23:51 - loss: 0.3617 - auroc: 0.79 - ETA: 23:50 - loss: 0.3631 - auroc: 0.79 - ETA: 23:48 - loss: 0.3636 - auroc: 0.78 - ETA: 23:47 - loss: 0.3648 - auroc: 0.78 - ETA: 23:45 - loss: 0.3651 - auroc: 0.78 - ETA: 23:42 - loss: 0.3659 - auroc: 0.78 - ETA: 23:40 - loss: 0.3655 - auroc: 0.78 - ETA: 23:38 - loss: 0.3673 - auroc: 0.78 - ETA: 23:37 - loss: 0.3675 - auroc: 0.78 - ETA: 23:39 - loss: 0.3691 - auroc: 0.78 - ETA: 23:38 - loss: 0.3723 - auroc: 0.77 - ETA: 23:36 - loss: 0.3728 - auroc: 0.78 - ETA: 23:34 - loss: 0.3739 - auroc: 0.77 - ETA: 23:33 - loss: 0.3723 - auroc: 0.77 - ETA: 23:32 - loss: 0.3720 - auroc: 0.78 - ETA: 23:32 - loss: 0.3729 - auroc: 0.77 - ETA: 23:30 - loss: 0.3705 - auroc: 0.77 - ETA: 23:29 - loss: 0.3679 - auroc: 0.78 - ETA: 23:28 - loss: 0.3663 - auroc: 0.78 - ETA: 23:27 - loss: 0.3690 - auroc: 0.78 - ETA: 23:25 - loss: 0.3684 - auroc: 0.77 - ETA: 23:24 - loss: 0.3675 - auroc: 0.77 - ETA: 23:23 - loss: 0.3686 - auroc: 0.77 - ETA: 23:21 - loss: 0.3694 - auroc: 0.77 - ETA: 23:20 - loss: 0.3679 - auroc: 0.77 - ETA: 23:18 - loss: 0.3708 - auroc: 0.77 - ETA: 23:20 - loss: 0.3719 - auroc: 0.77 - ETA: 23:18 - loss: 0.3712 - auroc: 0.77 - ETA: 23:17 - loss: 0.3726 - auroc: 0.77 - ETA: 23:16 - loss: 0.3735 - auroc: 0.77 - ETA: 23:14 - loss: 0.3742 - auroc: 0.77 - ETA: 23:13 - loss: 0.3760 - auroc: 0.77 - ETA: 23:12 - loss: 0.3742 - auroc: 0.77 - ETA: 23:10 - loss: 0.3766 - auroc: 0.77 - ETA: 23:09 - loss: 0.3761 - auroc: 0.77 - ETA: 23:07 - loss: 0.3767 - auroc: 0.77 - ETA: 23:06 - loss: 0.3756 - auroc: 0.77 - ETA: 23:05 - loss: 0.3751 - auroc: 0.77 - ETA: 23:04 - loss: 0.3745 - auroc: 0.77 - ETA: 23:02 - loss: 0.3755 - auroc: 0.77 - ETA: 23:01 - loss: 0.3741 - auroc: 0.77 - ETA: 22:59 - loss: 0.3742 - auroc: 0.77 - ETA: 22:58 - loss: 0.3753 - auroc: 0.77 - ETA: 22:56 - loss: 0.3747 - auroc: 0.77 - ETA: 22:55 - loss: 0.3772 - auroc: 0.77 - ETA: 22:53 - loss: 0.3761 - auroc: 0.77 - ETA: 22:52 - loss: 0.3746 - auroc: 0.77 - ETA: 22:50 - loss: 0.3752 - auroc: 0.77 - ETA: 22:49 - loss: 0.3747 - auroc: 0.77 - ETA: 22:49 - loss: 0.3750 - auroc: 0.77 - ETA: 22:47 - loss: 0.3747 - auroc: 0.77 - ETA: 22:46 - loss: 0.3745 - auroc: 0.77 - ETA: 22:45 - loss: 0.3761 - auroc: 0.77 - ETA: 22:44 - loss: 0.3764 - auroc: 0.77 - ETA: 22:42 - loss: 0.3771 - auroc: 0.77 - ETA: 22:41 - loss: 0.3776 - auroc: 0.77 - ETA: 22:40 - loss: 0.3779 - auroc: 0.77 - ETA: 22:39 - loss: 0.3781 - auroc: 0.77 - ETA: 22:37 - loss: 0.3769 - auroc: 0.77 - ETA: 22:36 - loss: 0.3769 - auroc: 0.77 - ETA: 22:35 - loss: 0.3772 - auroc: 0.77 - ETA: 22:34 - loss: 0.3789 - auroc: 0.77 - ETA: 22:33 - loss: 0.3796 - auroc: 0.77 - ETA: 22:32 - loss: 0.3809 - auroc: 0.77 - ETA: 22:30 - loss: 0.3801 - auroc: 0.77 - ETA: 22:29 - loss: 0.3791 - auroc: 0.77 - ETA: 22:28 - loss: 0.3803 - auroc: 0.77 - ETA: 22:26 - loss: 0.3811 - auroc: 0.77 - ETA: 22:25 - loss: 0.3827 - auroc: 0.76 - ETA: 22:24 - loss: 0.3816 - auroc: 0.77 - ETA: 22:23 - loss: 0.3819 - auroc: 0.77 - ETA: 22:22 - loss: 0.3817 - auroc: 0.77 - ETA: 22:20 - loss: 0.3822 - auroc: 0.76 - ETA: 22:19 - loss: 0.3826 - auroc: 0.76 - ETA: 22:18 - loss: 0.3835 - auroc: 0.76 - ETA: 22:16 - loss: 0.3829 - auroc: 0.77 - ETA: 22:15 - loss: 0.3815 - auroc: 0.77 - ETA: 22:14 - loss: 0.3803 - auroc: 0.77 - ETA: 22:13 - loss: 0.3805 - auroc: 0.77 - ETA: 22:12 - loss: 0.3808 - auroc: 0.77 - ETA: 22:10 - loss: 0.3800 - auroc: 0.77 - ETA: 22:09 - loss: 0.3810 - auroc: 0.77 - ETA: 22:07 - loss: 0.3815 - auroc: 0.77 - ETA: 22:06 - loss: 0.3812 - auroc: 0.77 - ETA: 22:04 - loss: 0.3808 - auroc: 0.77 - ETA: 22:03 - loss: 0.3799 - auroc: 0.77 - ETA: 22:02 - loss: 0.3798 - auroc: 0.77 - ETA: 22:00 - loss: 0.3790 - auroc: 0.77 - ETA: 21:59 - loss: 0.3795 - auroc: 0.77 - ETA: 21:57 - loss: 0.3790 - auroc: 0.77 - ETA: 21:56 - loss: 0.3790 - auroc: 0.77 - ETA: 21:55 - loss: 0.3784 - auroc: 0.77 - ETA: 21:53 - loss: 0.3781 - auroc: 0.77 - ETA: 21:52 - loss: 0.3773 - auroc: 0.77 - ETA: 21:51 - loss: 0.3769 - auroc: 0.77 - ETA: 21:50 - loss: 0.3770 - auroc: 0.77 - ETA: 21:49 - loss: 0.3759 - auroc: 0.77 - ETA: 21:47 - loss: 0.3752 - auroc: 0.77 - ETA: 21:46 - loss: 0.3744 - auroc: 0.77 - ETA: 21:45 - loss: 0.3746 - auroc: 0.77 - ETA: 21:44 - loss: 0.3750 - auroc: 0.77 - ETA: 21:43 - loss: 0.3769 - auroc: 0.77 - ETA: 21:41 - loss: 0.3771 - auroc: 0.77 - ETA: 21:40 - loss: 0.3778 - auroc: 0.77 - ETA: 21:39 - loss: 0.3793 - auroc: 0.77 - ETA: 21:38 - loss: 0.3796 - auroc: 0.77 - ETA: 21:37 - loss: 0.3805 - auroc: 0.77 - ETA: 21:35 - loss: 0.3811 - auroc: 0.77 - ETA: 21:34 - loss: 0.3808 - auroc: 0.77 - ETA: 21:33 - loss: 0.3804 - auroc: 0.77 - ETA: 21:31 - loss: 0.3805 - auroc: 0.77 - ETA: 21:30 - loss: 0.3817 - auroc: 0.76 - ETA: 21:29 - loss: 0.3820 - auroc: 0.76 - ETA: 21:28 - loss: 0.3815 - auroc: 0.76 - ETA: 21:27 - loss: 0.3818 - auroc: 0.76 - ETA: 21:25 - loss: 0.3815 - auroc: 0.76 - ETA: 21:24 - loss: 0.3807 - auroc: 0.76 - ETA: 21:23 - loss: 0.3809 - auroc: 0.76 - ETA: 21:22 - loss: 0.3803 - auroc: 0.76 - ETA: 21:21 - loss: 0.3797 - auroc: 0.76 - ETA: 21:20 - loss: 0.3797 - auroc: 0.76 - ETA: 21:18 - loss: 0.3788 - auroc: 0.76 - ETA: 21:17 - loss: 0.3795 - auroc: 0.76 - ETA: 21:16 - loss: 0.3793 - auroc: 0.76 - ETA: 21:15 - loss: 0.3790 - auroc: 0.76 - ETA: 21:14 - loss: 0.3797 - auroc: 0.76 - ETA: 21:13 - loss: 0.3794 - auroc: 0.77 - ETA: 21:12 - loss: 0.3784 - auroc: 0.77 - ETA: 21:10 - loss: 0.3790 - auroc: 0.76 - ETA: 21:09 - loss: 0.3791 - auroc: 0.76 - ETA: 21:08 - loss: 0.3793 - auroc: 0.76 - ETA: 21:07 - loss: 0.3789 - auroc: 0.77 - ETA: 21:06 - loss: 0.3788 - auroc: 0.76 - ETA: 21:05 - loss: 0.3787 - auroc: 0.76 - ETA: 21:03 - loss: 0.3791 - auroc: 0.76 - ETA: 21:02 - loss: 0.3796 - auroc: 0.76 - ETA: 21:01 - loss: 0.3797 - auroc: 0.76 - ETA: 20:59 - loss: 0.3799 - auroc: 0.76 - ETA: 20:58 - loss: 0.3795 - auroc: 0.76 - ETA: 20:57 - loss: 0.3790 - auroc: 0.76 - ETA: 20:56 - loss: 0.3788 - auroc: 0.76 - ETA: 20:54 - loss: 0.3785 - auroc: 0.76 - ETA: 20:53 - loss: 0.3781 - auroc: 0.77 - ETA: 20:52 - loss: 0.3774 - auroc: 0.77 - ETA: 20:51 - loss: 0.3771 - auroc: 0.77 - ETA: 20:49 - loss: 0.3772 - auroc: 0.77 - ETA: 20:48 - loss: 0.3774 - auroc: 0.77 - ETA: 20:47 - loss: 0.3777 - auroc: 0.77 - ETA: 20:46 - loss: 0.3780 - auroc: 0.77 - ETA: 20:45 - loss: 0.3787 - auroc: 0.77 - ETA: 20:44 - loss: 0.3786 - auroc: 0.77 - ETA: 20:43 - loss: 0.3779 - auroc: 0.77 - ETA: 20:42 - loss: 0.3785 - auroc: 0.77 - ETA: 20:41 - loss: 0.3785 - auroc: 0.77 - ETA: 20:39 - loss: 0.3784 - auroc: 0.77 - ETA: 20:38 - loss: 0.3778 - auroc: 0.77 - ETA: 20:37 - loss: 0.3774 - auroc: 0.77 - ETA: 20:36 - loss: 0.3777 - auroc: 0.76 - ETA: 20:35 - loss: 0.3773 - auroc: 0.76 - ETA: 20:34 - loss: 0.3775 - auroc: 0.76 - ETA: 20:32 - loss: 0.3771 - auroc: 0.76 - ETA: 20:31 - loss: 0.3769 - auroc: 0.76 - ETA: 20:30 - loss: 0.3769 - auroc: 0.770124832/76473 [========>.....................] - ETA: 20:29 - loss: 0.3767 - auroc: 0.76 - ETA: 20:27 - loss: 0.3765 - auroc: 0.76 - ETA: 20:26 - loss: 0.3761 - auroc: 0.77 - ETA: 20:25 - loss: 0.3759 - auroc: 0.76 - ETA: 20:23 - loss: 0.3758 - auroc: 0.76 - ETA: 20:22 - loss: 0.3757 - auroc: 0.76 - ETA: 20:21 - loss: 0.3758 - auroc: 0.76 - ETA: 20:20 - loss: 0.3761 - auroc: 0.76 - ETA: 20:18 - loss: 0.3765 - auroc: 0.76 - ETA: 20:17 - loss: 0.3763 - auroc: 0.76 - ETA: 20:16 - loss: 0.3766 - auroc: 0.76 - ETA: 20:15 - loss: 0.3761 - auroc: 0.76 - ETA: 20:13 - loss: 0.3762 - auroc: 0.76 - ETA: 20:12 - loss: 0.3764 - auroc: 0.76 - ETA: 20:11 - loss: 0.3766 - auroc: 0.76 - ETA: 20:10 - loss: 0.3762 - auroc: 0.76 - ETA: 20:08 - loss: 0.3763 - auroc: 0.76 - ETA: 20:07 - loss: 0.3761 - auroc: 0.76 - ETA: 20:05 - loss: 0.3763 - auroc: 0.76 - ETA: 20:04 - loss: 0.3763 - auroc: 0.76 - 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val_loss: 0.3775 - val_auroc: 0.7483

Epoch 00004: val_loss improved from 0.37759 to 0.37754, saving model to model_1.h5

Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.
Epoch 5/20
12416/76473 [===>..........................] - ETA: 24:49 - loss: 0.2768 - auroc: 0.80 - ETA: 24:40 - loss: 0.3351 - auroc: 0.75 - ETA: 24:29 - loss: 0.3677 - auroc: 0.76 - ETA: 24:28 - loss: 0.3726 - auroc: 0.77 - ETA: 24:33 - loss: 0.3880 - auroc: 0.74 - ETA: 24:26 - loss: 0.3909 - auroc: 0.75 - ETA: 24:32 - loss: 0.3810 - auroc: 0.76 - ETA: 24:32 - loss: 0.3799 - auroc: 0.77 - ETA: 24:32 - loss: 0.3735 - auroc: 0.76 - ETA: 24:25 - loss: 0.3784 - auroc: 0.76 - ETA: 24:24 - loss: 0.3720 - auroc: 0.76 - ETA: 24:29 - loss: 0.3630 - auroc: 0.75 - ETA: 24:25 - loss: 0.3626 - auroc: 0.76 - ETA: 24:21 - loss: 0.3586 - auroc: 0.76 - ETA: 24:19 - loss: 0.3570 - auroc: 0.77 - ETA: 24:17 - loss: 0.3565 - auroc: 0.76 - ETA: 24:15 - loss: 0.3569 - auroc: 0.75 - ETA: 24:15 - loss: 0.3586 - auroc: 0.76 - ETA: 24:12 - loss: 0.3639 - auroc: 0.76 - ETA: 24:10 - loss: 0.3580 - auroc: 0.75 - ETA: 24:08 - loss: 0.3631 - auroc: 0.76 - ETA: 24:06 - loss: 0.3633 - auroc: 0.76 - ETA: 24:10 - loss: 0.3593 - auroc: 0.76 - ETA: 24:08 - loss: 0.3615 - auroc: 0.76 - ETA: 24:08 - loss: 0.3606 - auroc: 0.76 - ETA: 24:05 - loss: 0.3618 - auroc: 0.76 - ETA: 24:05 - loss: 0.3633 - auroc: 0.76 - ETA: 24:07 - loss: 0.3616 - auroc: 0.76 - ETA: 24:04 - loss: 0.3585 - auroc: 0.76 - ETA: 24:03 - loss: 0.3550 - auroc: 0.76 - ETA: 24:01 - loss: 0.3549 - auroc: 0.76 - ETA: 23:59 - loss: 0.3525 - auroc: 0.76 - ETA: 23:57 - loss: 0.3511 - auroc: 0.77 - ETA: 23:56 - loss: 0.3535 - auroc: 0.77 - ETA: 23:54 - loss: 0.3597 - auroc: 0.76 - ETA: 23:53 - loss: 0.3568 - auroc: 0.76 - ETA: 23:51 - loss: 0.3537 - auroc: 0.77 - ETA: 23:49 - loss: 0.3580 - auroc: 0.76 - ETA: 23:48 - loss: 0.3602 - auroc: 0.76 - ETA: 23:47 - loss: 0.3593 - auroc: 0.77 - ETA: 23:46 - loss: 0.3629 - auroc: 0.77 - ETA: 23:44 - loss: 0.3620 - auroc: 0.77 - ETA: 23:43 - loss: 0.3615 - auroc: 0.77 - ETA: 23:42 - loss: 0.3603 - auroc: 0.77 - ETA: 23:40 - loss: 0.3594 - auroc: 0.77 - ETA: 23:39 - loss: 0.3609 - auroc: 0.77 - ETA: 23:38 - loss: 0.3628 - auroc: 0.77 - 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ETA: 21:06 - loss: 0.3595 - auroc: 0.77 - ETA: 21:05 - loss: 0.3594 - auroc: 0.77 - ETA: 21:04 - loss: 0.3590 - auroc: 0.77 - ETA: 21:02 - loss: 0.3591 - auroc: 0.77 - ETA: 21:01 - loss: 0.3591 - auroc: 0.77 - ETA: 21:00 - loss: 0.3588 - auroc: 0.77 - ETA: 20:58 - loss: 0.3586 - auroc: 0.77 - ETA: 20:57 - loss: 0.3589 - auroc: 0.77 - ETA: 20:56 - loss: 0.3596 - auroc: 0.77 - ETA: 20:55 - loss: 0.3595 - auroc: 0.77 - ETA: 20:54 - loss: 0.3591 - auroc: 0.77 - ETA: 20:52 - loss: 0.3592 - auroc: 0.77 - ETA: 20:51 - loss: 0.3593 - auroc: 0.77 - ETA: 20:50 - loss: 0.3592 - auroc: 0.77 - ETA: 20:49 - loss: 0.3595 - auroc: 0.77 - ETA: 20:48 - loss: 0.3592 - auroc: 0.77 - ETA: 20:46 - loss: 0.3591 - auroc: 0.77 - ETA: 20:45 - loss: 0.3590 - auroc: 0.77 - ETA: 20:43 - loss: 0.3585 - auroc: 0.77 - ETA: 20:42 - loss: 0.3583 - auroc: 0.77 - ETA: 20:41 - loss: 0.3580 - auroc: 0.77 - ETA: 20:40 - loss: 0.3583 - auroc: 0.77 - ETA: 20:38 - loss: 0.3582 - auroc: 0.77 - ETA: 20:37 - loss: 0.3581 - auroc: 0.77 - ETA: 20:35 - loss: 0.3588 - auroc: 0.77 - ETA: 20:34 - loss: 0.3589 - auroc: 0.77 - ETA: 20:33 - loss: 0.3588 - auroc: 0.777924832/76473 [========>.....................] - ETA: 20:32 - loss: 0.3589 - auroc: 0.77 - ETA: 20:31 - loss: 0.3583 - auroc: 0.77 - ETA: 20:29 - loss: 0.3584 - auroc: 0.77 - ETA: 20:28 - loss: 0.3583 - auroc: 0.77 - ETA: 20:27 - loss: 0.3579 - auroc: 0.77 - ETA: 20:25 - loss: 0.3573 - auroc: 0.77 - ETA: 20:24 - loss: 0.3572 - auroc: 0.77 - ETA: 20:23 - loss: 0.3575 - auroc: 0.77 - ETA: 20:22 - loss: 0.3572 - auroc: 0.77 - ETA: 20:20 - loss: 0.3570 - auroc: 0.77 - ETA: 20:19 - loss: 0.3571 - auroc: 0.77 - ETA: 20:18 - loss: 0.3573 - auroc: 0.77 - ETA: 20:17 - loss: 0.3575 - auroc: 0.77 - ETA: 20:16 - loss: 0.3575 - auroc: 0.77 - ETA: 20:14 - loss: 0.3576 - auroc: 0.77 - ETA: 20:13 - loss: 0.3583 - auroc: 0.77 - ETA: 20:12 - loss: 0.3588 - auroc: 0.77 - ETA: 20:10 - loss: 0.3588 - auroc: 0.77 - ETA: 20:09 - loss: 0.3586 - auroc: 0.77 - ETA: 20:08 - loss: 0.3586 - auroc: 0.77 - 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val_loss: 0.3788 - val_auroc: 0.7459

Epoch 00005: val_loss did not improve from 0.37754
Restoring model weights from the end of the best epoch

Epoch 00005: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.
Epoch 00005: early stopping

AUC Score

In [199]:
history = model_1.predict(test_data_1)
print("AUC score is {}".format(roc_auc_score(Y_test,history)))

AUC score is 0.7478621969408079

Tensor Board Images

In [205]:
from IPython.display import Image
Image(filename='Model_1_1.jpg') #auroc
Out[205]:

Loss

In [207]:
Image(filename='Model_1_2.jpg') #loss
Out[207]:

val_auroc

In [208]:
Image(filename='Model_1_3.jpg') #val_auroc
Out[208]:

val_loss

In [209]:
Image(filename='Model_1_4.jpg') #val_loss
Out[209]:

Model 2

For Model 2 we will be using the layers from Model 1 and will taking the top frequency words based on essay layer

Calculating the idf values

In [46]:
# Filtering Text Data based on idf values
tfidf = TfidfVectorizer()
combine_tfidf = tfidf.fit_transform(x_train["essay"])
# converting to dictionary
combine_dict = dict(zip(tfidf.get_feature_names(),list(tfidf.idf_)))
In [47]:
d = {'word': tfidf.get_feature_names() , 'idf_value': tfidf.idf_}
tfidf_df = pd.DataFrame(data=d)
tfidf_df.set_index = 'idf_value'
In [48]:
tfidf_df.sort_values(by ='idf_value' , inplace=True  )
tfidf_df.set_index = 'idf_value'
tfidf_df.head(2)
Out[48]:
word idf_value
42141 students 1.008009
29358 nannan 1.046039
In [49]:
print("Min tf-idf value is: ",min(tfidf_df['idf_value']))
print("Max tf-idf value is: ",max(tfidf_df['idf_value']))

Min tf-idf value is:  1.0080085502013645
Max tf-idf value is:  11.551558912204982
In [50]:
fig , ax = plt.subplots()
ax = sns.violinplot(y=tfidf_df['idf_value'])
ax.set_xlabel("violin plot of IDF values")
plt.show()
In [51]:
print("Quantiles: 0-25-50-75")
print(np.percentile(tfidf_df['idf_value'],np.arange(0, 100, 25)))
print(np.percentile(tfidf_df['idf_value'] , 100))

Quantiles: 0-25-50-75
[ 1.00800855  9.53665589 11.1460938  11.55155891]
11.551558912204982
In [52]:
# we are only considering the values between 2 and 11
mask =   (tfidf_df['idf_value']>=2) & (tfidf_df['idf_value'] <=11)
tfidf_selected = tfidf_df[mask]
print(tfidf_selected.shape)

(24391, 2)
In [53]:
corpus_selected = tfidf_selected['word'].tolist()
In [54]:
tokenizer = Tokenizer()
tokenizer.fit_on_texts(corpus_selected)
x_train['IntegerEncoding_2']  = tokenizer.texts_to_sequences(x_train['essay'].values)
x_test['IntegerEncoding_2']   = tokenizer.texts_to_sequences(x_test['essay'].values)
In [55]:
max_vocabulary = len(tokenizer.word_index)
In [57]:
# truncate and/or pad input sequences
# We will be using this as text input to our model
max_review_length = 500
text_padded_input_train = sequence.pad_sequences(x_train['IntegerEncoding_2'].values, maxlen=max_review_length , padding='post' )
text_padded_input_test  = sequence.pad_sequences(x_test['IntegerEncoding_2'].values, maxlen=max_review_length , padding='post' )
In [58]:
import pickle
with open('glove_vectors', 'rb') as f:
    glove = pickle.load(f)
    glove_words =  set(glove.keys())
In [59]:
from numpy import zeros
# create a weight matrix for words in training docs
embedding_matrix = zeros((max_vocabulary+1, 300))
for word, i in tokenizer.word_index.items():
    if word in glove_words:
       embedding_vector = glove[word]
       embedding_matrix[i] = embedding_vector

Essay layer

In [60]:
# text_input: meant to receive sequences of 500 integers, between 1 and max_vocabulary.
# Note that we can name any layer by passing it a "name" argument.
text_input =  Input(shape=(500,), dtype='int32', name='text_input')
# This embedding layer will encode the input sequence
# into a sequence of dense 300 dimensional vectors.
x = Embedding(output_dim=300, input_dim=max_vocabulary+1, input_length=500 , weights=[embedding_matrix] , trainable=False)(text_input)
# A LSTM will transform the vector sequence into a single vector,
# containing information about the entire sequence
x = LSTM(32 , return_sequences=True ,kernel_regularizer=regularizers.l2(0.001) )(x)
flatten_12= Flatten()(x)
In [61]:
# Concatinate all the flatten layers and numerical layer
print("Constructing Model2")
x_concat = concatenate([flatten_12 , flatten_2 , flatten_3 ,flatten_4 , flatten_5 , flatten_6 , num_feats])
x = Dense(64, activation="relu", kernel_initializer="he_normal" ,kernel_regularizer=regularizers.l2(0.001))(x_concat)
x=Dropout(0.2)(x)
x = Dense(32,activation="relu",kernel_initializer="he_normal" ,kernel_regularizer=regularizers.l2(0.001))(x)
x = BatchNormalization()(x)
x=Dropout(0.2)(x)
x = Dense(16,activation="relu", kernel_initializer="he_normal" ,kernel_regularizer=regularizers.l2(0.001))(x)
output = Dense(2, activation='sigmoid', name='output')(x)
model_2 = Model(inputs=[text_input,input_prefix,input_state,input_grade,
                        input_subcat,input_subcat_1,num_feats],outputs=[output])
print(model_2.summary())

Constructing Model2
Model: "model_2"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
text_input (InputLayer)         (None, 500)          0                                            
__________________________________________________________________________________________________
embedding_2 (Embedding)         (None, 500, 300)     7317600     text_input[0][0]                 
__________________________________________________________________________________________________
teacher_prefix (InputLayer)     (None, 1)            0                                            
__________________________________________________________________________________________________
school_prefix (InputLayer)      (None, 1)            0                                            
__________________________________________________________________________________________________
grade_cat (InputLayer)          (None, 1)            0                                            
__________________________________________________________________________________________________
sub_cat (InputLayer)            (None, 1)            0                                            
__________________________________________________________________________________________________
sub_cat_1 (InputLayer)          (None, 1)            0                                            
__________________________________________________________________________________________________
lstm_2 (LSTM)                   (None, 500, 32)      42624       embedding_2[0][0]                
__________________________________________________________________________________________________
emb_pre (Embedding)             (None, 1, 3)         15          teacher_prefix[0][0]             
__________________________________________________________________________________________________
emb_state (Embedding)           (None, 1, 26)        1326        school_prefix[0][0]              
__________________________________________________________________________________________________
emb_grade (Embedding)           (None, 1, 2)         8           grade_cat[0][0]                  
__________________________________________________________________________________________________
emb_subcat (Embedding)          (None, 1, 26)        1326        sub_cat[0][0]                    
__________________________________________________________________________________________________
emb_subcat_1 (Embedding)        (None, 1, 50)        19650       sub_cat_1[0][0]                  
__________________________________________________________________________________________________
flatten_7 (Flatten)             (None, 16000)        0           lstm_2[0][0]                     
__________________________________________________________________________________________________
flatten_2 (Flatten)             (None, 3)            0           emb_pre[0][0]                    
__________________________________________________________________________________________________
flatten_3 (Flatten)             (None, 26)           0           emb_state[0][0]                  
__________________________________________________________________________________________________
flatten_4 (Flatten)             (None, 2)            0           emb_grade[0][0]                  
__________________________________________________________________________________________________
flatten_5 (Flatten)             (None, 26)           0           emb_subcat[0][0]                 
__________________________________________________________________________________________________
flatten_6 (Flatten)             (None, 50)           0           emb_subcat_1[0][0]               
__________________________________________________________________________________________________
numerical_features (InputLayer) (None, 3)            0                                            
__________________________________________________________________________________________________
concatenate_2 (Concatenate)     (None, 16110)        0           flatten_7[0][0]                  
                                                                 flatten_2[0][0]                  
                                                                 flatten_3[0][0]                  
                                                                 flatten_4[0][0]                  
                                                                 flatten_5[0][0]                  
                                                                 flatten_6[0][0]                  
                                                                 numerical_features[0][0]         
__________________________________________________________________________________________________
dense_5 (Dense)                 (None, 64)           1031104     concatenate_2[0][0]              
__________________________________________________________________________________________________
dropout_3 (Dropout)             (None, 64)           0           dense_5[0][0]                    
__________________________________________________________________________________________________
dense_6 (Dense)                 (None, 32)           2080        dropout_3[0][0]                  
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 32)           128         dense_6[0][0]                    
__________________________________________________________________________________________________
dropout_4 (Dropout)             (None, 32)           0           batch_normalization_2[0][0]      
__________________________________________________________________________________________________
dense_7 (Dense)                 (None, 16)           528         dropout_4[0][0]                  
__________________________________________________________________________________________________
output (Dense)                  (None, 2)            34          dense_7[0][0]                    
==================================================================================================
Total params: 8,416,423
Trainable params: 1,098,759
Non-trainable params: 7,317,664
__________________________________________________________________________________________________
None
In [62]:
train_data_1 = [text_padded_input_train,encoder_prefix_train,encoder_state_train,
              encoder_grade_train,encoder_subcat_train,encoder_subcat_1_train,norm_train]
test_data_1 = [text_padded_input_test,encoder_prefix_test,encoder_state_test,encoder_grade_test,
             encoder_subcat_test,encoder_subcat_1_test,norm_test]
In [63]:
model_2.compile(loss='binary_crossentropy', optimizer='adam',metrics=[auroc])

Keeping verbosity 0

In [43]:
history_2 = model_2.fit(train_data_1,Y_train,batch_size=64,
                        epochs=20,validation_data=(test_data_1,Y_test),callbacks=callbacks_1, verbose = 0)

Auc Score

In [233]:
history = model_2.predict(test_data_1)
print("AUC score is {}".format(roc_auc_score(Y_test,history)))

AUC score is 0.7535608117500832

Tensor board Images

In [2]:
from IPython.display import Image
Image(filename='Model_2_1.jpg') #auroc
Out[2]:
In [3]:
Image(filename='Model_2_2.jpg') #loss
Out[3]:
In [4]:
Image(filename='Model_2_3.jpg') #val_auroc
Out[4]:
In [5]:
Image(filename='Model_2_4.jpg') #val_loss
Out[5]:

Model 3

In [64]:
print('Shape of train data :', encoded_train.shape)
print('Shape of test data :', encoded_test.shape)

Shape of train data : (76473, 350)
Shape of test data : (32775, 350)

Vectorizing all the categorical features using CountVectorizer

In [65]:
# Teacher prefix
vect = CountVectorizer(binary=True)
vect.fit(x_train["teacher_prefix"])
train_prefix = vect.transform(x_train["teacher_prefix"])
test_prefix = vect.transform(x_test["teacher_prefix"])
In [66]:
# School State
vect = CountVectorizer(binary=True)
vect.fit(x_train["school_state"])
train_state = vect.transform(x_train["school_state"])
test_state = vect.transform(x_test["school_state"])
In [67]:
# For project_grade_category
vect = CountVectorizer(binary=True)
vect.fit(x_train["project_grade_category"])
train_grade = vect.transform(x_train["project_grade_category"])
test_grade = vect.transform(x_test["project_grade_category"])
In [68]:
# For clean_categories
vect = CountVectorizer(binary=True)
vect.fit(x_train["clean_categories"])
train_subcat = vect.transform(x_train["clean_categories"])
test_subcat = vect.transform(x_test["clean_categories"])
In [69]:
# For clean_subcategories
vect = CountVectorizer(binary=True)
vect.fit(x_train["clean_subcategories"])
train_subcat_1 = vect.transform(x_train["clean_subcategories"])
test_subcat_1 = vect.transform(x_test["clean_subcategories"])
In [70]:
# Now we will prepare numerical features for our model
num_train_1=x_train['len_essay'].values.reshape(-1, 1)
num_train_2=x_train['price'].values.reshape(-1, 1)
num_train_3=x_train['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1)
num_test_1=x_test['len_essay'].values.reshape(-1, 1)
num_test_2=x_test['price'].values.reshape(-1, 1)
num_test_3=x_test['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1)
num_train=np.concatenate((num_train_1,num_train_2,num_train_3),axis=1)
num_test=np.concatenate((num_test_1,num_test_2,num_test_3),axis=1)
In [71]:
from sklearn.preprocessing import StandardScaler
norm=StandardScaler()
norm_train=norm.fit_transform(num_train)
norm_test=norm.transform(num_test)
In [72]:
from scipy.sparse import hstack
cat_train = hstack([train_prefix,train_state,train_grade,train_subcat,train_subcat_1]).todense()
cat_test = hstack([test_prefix,test_state,test_grade,test_subcat,test_subcat_1]).todense()
In [73]:
x_train = np.hstack((cat_train,norm_train))
x_test = np.hstack((cat_test,norm_test))
In [74]:
x_train.shape
Out[74]:
(76473, 102)
In [75]:
expend_train = np.expand_dims(x_train,2)
expend_test = np.expand_dims(x_test,2)
In [76]:
expend_train.shape
Out[76]:
(76473, 102, 1)

Model Architecture

In [77]:
#https://keras.io/layers/convolutional/
inp_conv =  Input(shape=(102, 1),name='features_all')
x1 = Conv1D(filters=128, kernel_size=3, activation='relu',kernel_initializer="he_normal")(inp_conv)
x1 = Conv1D(filters=128, kernel_size=3, activation='relu',kernel_initializer="he_normal")(x1)
x2 = Flatten()(x1)
In [78]:
print("Building Model-3")
x_concatenate = concatenate([flatten_1,x2])
x = Dense(16,activation="relu",kernel_initializer="he_normal",kernel_regularizer=regularizers.l2(0.001))(x_concatenate)
x=Dropout(0.5)(x)
x = Dense(8,activation="relu",kernel_initializer="he_normal",kernel_regularizer=regularizers.l2(0.001))(x)
x=Dropout(0.3)(x)
x = Dense(4,activation="relu",kernel_initializer="he_normal",kernel_regularizer=regularizers.l2(0.001))(x)
output = Dense(2, activation='sigmoid', name='output')(x)
model_3 = Model(inputs=[input_text,inp_conv],outputs=[output])

Building Model-3
In [79]:
train_data_3 = [encoded_train,expend_train]
test_data_3 = [encoded_test,expend_test]
from keras.utils import np_utils
Y_train = np_utils.to_categorical(y_train, 2) 
Y_test = np_utils.to_categorical(y_test, 2)
In [80]:
checkpoint_3 = ModelCheckpoint("model_3.h5",monitor="val_auroc",mode="max",save_best_only = True,verbose=1)

earlystop_3 = EarlyStopping(monitor = 'val_loss',mode="min",min_delta = 0,patience = 2,verbose = 1,restore_best_weights = True)

reduce_lr_3 = ReduceLROnPlateau(monitor = 'val_auroc', factor = 0.2, patience = 2, verbose = 1, min_delta = 0.0001)

tensorboard_3 = TensorBoard(log_dir='graph_3', histogram_freq=0, batch_size=512, write_graph=True, write_grads=True, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None, embeddings_data=None, update_freq='epoch')

callbacks_3 = [tensorboard_3,checkpoint_3,earlystop_3,reduce_lr_3]
In [82]:
model_3.summary()

Model: "model_3"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_text (InputLayer)         (None, 350)          0                                            
__________________________________________________________________________________________________
features_all (InputLayer)       (None, 102, 1)       0                                            
__________________________________________________________________________________________________
embedding_1 (Embedding)         (None, 350, 300)     1500000     input_text[0][0]                 
__________________________________________________________________________________________________
conv1d_1 (Conv1D)               (None, 100, 128)     512         features_all[0][0]               
__________________________________________________________________________________________________
lstm_1 (LSTM)                   (None, 350, 4)       4880        embedding_1[0][0]                
__________________________________________________________________________________________________
conv1d_2 (Conv1D)               (None, 98, 128)      49280       conv1d_1[0][0]                   
__________________________________________________________________________________________________
flatten_1 (Flatten)             (None, 1400)         0           lstm_1[0][0]                     
__________________________________________________________________________________________________
flatten_8 (Flatten)             (None, 12544)        0           conv1d_2[0][0]                   
__________________________________________________________________________________________________
concatenate_3 (Concatenate)     (None, 13944)        0           flatten_1[0][0]                  
                                                                 flatten_8[0][0]                  
__________________________________________________________________________________________________
dense_8 (Dense)                 (None, 16)           223120      concatenate_3[0][0]              
__________________________________________________________________________________________________
dropout_5 (Dropout)             (None, 16)           0           dense_8[0][0]                    
__________________________________________________________________________________________________
dense_9 (Dense)                 (None, 8)            136         dropout_5[0][0]                  
__________________________________________________________________________________________________
dropout_6 (Dropout)             (None, 8)            0           dense_9[0][0]                    
__________________________________________________________________________________________________
dense_10 (Dense)                (None, 4)            36          dropout_6[0][0]                  
__________________________________________________________________________________________________
output (Dense)                  (None, 2)            10          dense_10[0][0]                   
==================================================================================================
Total params: 1,777,974
Trainable params: 277,974
Non-trainable params: 1,500,000
__________________________________________________________________________________________________
In [81]:
model_3.compile(optimizer='Adam', loss='binary_crossentropy', metrics=[auroc])
In [99]:
history_3 = model_3.fit(train_data_3,Y_train,batch_size=64,epochs=20,validation_data=(test_data_3,Y_test),callbacks=callbacks_3)

Train on 76473 samples, validate on 32775 samples
Epoch 1/20
12352/76473 [===>..........................] - ETA: 27:47 - loss: 0.4128 - auroc: 0.74 - ETA: 17:17 - loss: 0.3817 - auroc: 0.72 - ETA: 13:39 - loss: 0.3799 - auroc: 0.73 - ETA: 11:51 - loss: 0.3649 - auroc: 0.75 - ETA: 10:40 - loss: 0.4227 - auroc: 0.76 - ETA: 9:57 - loss: 0.4260 - auroc: 0.7686 - ETA: 9:30 - loss: 0.4294 - auroc: 0.760 - ETA: 9:06 - loss: 0.4191 - auroc: 0.765 - ETA: 8:45 - loss: 0.4128 - auroc: 0.762 - ETA: 8:33 - loss: 0.3962 - auroc: 0.760 - ETA: 8:19 - loss: 0.3990 - auroc: 0.744 - ETA: 8:10 - loss: 0.3973 - auroc: 0.742 - ETA: 8:03 - loss: 0.3995 - auroc: 0.737 - ETA: 7:55 - loss: 0.4010 - auroc: 0.733 - ETA: 7:49 - loss: 0.4051 - auroc: 0.736 - ETA: 7:43 - loss: 0.4068 - auroc: 0.732 - ETA: 7:40 - loss: 0.4027 - auroc: 0.731 - ETA: 7:35 - loss: 0.3971 - auroc: 0.736 - ETA: 7:32 - loss: 0.3966 - auroc: 0.737 - ETA: 7:30 - loss: 0.3972 - auroc: 0.726 - ETA: 7:26 - loss: 0.3971 - auroc: 0.728 - ETA: 7:25 - loss: 0.3948 - auroc: 0.729 - ETA: 7:22 - loss: 0.3927 - auroc: 0.725 - ETA: 7:19 - loss: 0.3934 - auroc: 0.721 - ETA: 7:16 - loss: 0.3970 - auroc: 0.722 - ETA: 7:13 - loss: 0.3930 - auroc: 0.726 - ETA: 7:10 - loss: 0.3987 - auroc: 0.717 - ETA: 7:09 - loss: 0.3936 - auroc: 0.725 - ETA: 7:06 - loss: 0.3893 - auroc: 0.731 - ETA: 7:05 - loss: 0.3938 - auroc: 0.732 - ETA: 7:04 - loss: 0.3951 - auroc: 0.733 - ETA: 7:02 - loss: 0.3919 - auroc: 0.734 - ETA: 7:00 - loss: 0.3903 - auroc: 0.740 - ETA: 6:59 - loss: 0.3894 - auroc: 0.745 - ETA: 6:57 - loss: 0.3915 - auroc: 0.742 - ETA: 6:55 - loss: 0.3913 - auroc: 0.740 - ETA: 6:54 - loss: 0.3882 - auroc: 0.742 - ETA: 6:53 - loss: 0.3847 - auroc: 0.742 - ETA: 6:51 - loss: 0.3845 - auroc: 0.739 - ETA: 6:51 - loss: 0.3840 - auroc: 0.740 - ETA: 6:49 - loss: 0.3832 - auroc: 0.743 - ETA: 6:48 - loss: 0.3824 - auroc: 0.744 - ETA: 6:46 - loss: 0.3818 - auroc: 0.745 - ETA: 6:46 - loss: 0.3873 - auroc: 0.746 - ETA: 6:46 - loss: 0.3844 - auroc: 0.748 - ETA: 6:45 - loss: 0.3846 - auroc: 0.750 - ETA: 6:44 - loss: 0.3850 - auroc: 0.751 - ETA: 6:44 - loss: 0.3843 - auroc: 0.752 - ETA: 6:43 - loss: 0.3813 - auroc: 0.755 - ETA: 6:42 - loss: 0.3815 - auroc: 0.753 - ETA: 6:42 - loss: 0.3816 - auroc: 0.753 - ETA: 6:42 - loss: 0.3809 - auroc: 0.753 - ETA: 6:41 - loss: 0.3819 - auroc: 0.750 - ETA: 6:40 - loss: 0.3828 - auroc: 0.752 - ETA: 6:40 - loss: 0.3846 - auroc: 0.751 - ETA: 6:39 - loss: 0.3857 - auroc: 0.751 - ETA: 6:39 - loss: 0.3855 - auroc: 0.750 - ETA: 6:38 - loss: 0.3870 - auroc: 0.750 - ETA: 6:37 - loss: 0.3884 - auroc: 0.746 - ETA: 6:37 - loss: 0.3879 - auroc: 0.746 - ETA: 6:36 - loss: 0.3883 - auroc: 0.747 - ETA: 6:35 - loss: 0.3890 - auroc: 0.746 - ETA: 6:35 - loss: 0.3871 - auroc: 0.749 - ETA: 6:34 - loss: 0.3866 - auroc: 0.750 - ETA: 6:33 - loss: 0.3872 - auroc: 0.748 - ETA: 6:33 - loss: 0.3869 - auroc: 0.748 - ETA: 6:33 - loss: 0.3870 - auroc: 0.749 - ETA: 6:32 - loss: 0.3898 - auroc: 0.747 - ETA: 6:31 - loss: 0.3877 - auroc: 0.747 - ETA: 6:31 - loss: 0.3881 - auroc: 0.745 - ETA: 6:30 - loss: 0.3882 - auroc: 0.745 - ETA: 6:30 - loss: 0.3899 - auroc: 0.744 - ETA: 6:29 - loss: 0.3901 - auroc: 0.744 - ETA: 6:28 - loss: 0.3895 - auroc: 0.745 - ETA: 6:28 - loss: 0.3888 - auroc: 0.743 - ETA: 6:27 - loss: 0.3894 - auroc: 0.744 - ETA: 6:27 - loss: 0.3890 - auroc: 0.742 - ETA: 6:26 - loss: 0.3892 - auroc: 0.742 - ETA: 6:25 - loss: 0.3908 - auroc: 0.741 - ETA: 6:25 - loss: 0.3910 - auroc: 0.741 - ETA: 6:24 - loss: 0.3909 - auroc: 0.741 - ETA: 6:24 - loss: 0.3904 - auroc: 0.742 - ETA: 6:23 - loss: 0.3901 - auroc: 0.742 - ETA: 6:22 - loss: 0.3905 - auroc: 0.742 - ETA: 6:22 - loss: 0.3911 - auroc: 0.742 - ETA: 6:21 - loss: 0.3914 - auroc: 0.744 - ETA: 6:21 - loss: 0.3929 - auroc: 0.745 - ETA: 6:20 - loss: 0.3935 - auroc: 0.745 - ETA: 6:20 - loss: 0.3928 - auroc: 0.746 - ETA: 6:19 - loss: 0.3923 - auroc: 0.744 - ETA: 6:18 - loss: 0.3918 - auroc: 0.746 - ETA: 6:18 - loss: 0.3917 - auroc: 0.744 - ETA: 6:17 - loss: 0.3915 - auroc: 0.742 - ETA: 6:17 - loss: 0.3919 - auroc: 0.742 - ETA: 6:16 - loss: 0.3918 - auroc: 0.742 - 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ETA: 6:05 - loss: 0.3866 - auroc: 0.745 - ETA: 6:05 - loss: 0.3859 - auroc: 0.746 - ETA: 6:05 - loss: 0.3865 - auroc: 0.746 - ETA: 6:05 - loss: 0.3863 - auroc: 0.746 - ETA: 6:04 - loss: 0.3869 - auroc: 0.745 - ETA: 6:04 - loss: 0.3858 - auroc: 0.746 - ETA: 6:04 - loss: 0.3855 - auroc: 0.746 - ETA: 6:03 - loss: 0.3848 - auroc: 0.747 - ETA: 6:03 - loss: 0.3844 - auroc: 0.747 - ETA: 6:02 - loss: 0.3836 - auroc: 0.748 - ETA: 6:02 - loss: 0.3829 - auroc: 0.748 - ETA: 6:02 - loss: 0.3835 - auroc: 0.748 - ETA: 6:01 - loss: 0.3829 - auroc: 0.749 - ETA: 6:01 - loss: 0.3829 - auroc: 0.749 - ETA: 6:01 - loss: 0.3824 - auroc: 0.750 - ETA: 6:00 - loss: 0.3817 - auroc: 0.751 - ETA: 6:00 - loss: 0.3804 - auroc: 0.752 - ETA: 5:59 - loss: 0.3794 - auroc: 0.752 - ETA: 5:59 - loss: 0.3803 - auroc: 0.752 - ETA: 5:58 - loss: 0.3797 - auroc: 0.752 - ETA: 5:58 - loss: 0.3790 - auroc: 0.753 - ETA: 5:57 - loss: 0.3791 - auroc: 0.754 - ETA: 5:57 - loss: 0.3795 - auroc: 0.754 - ETA: 5:57 - loss: 0.3790 - auroc: 0.754 - 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auroc: 0.75 - ETA: 22s - loss: 0.3762 - auroc: 0.75 - ETA: 21s - loss: 0.3762 - auroc: 0.75 - ETA: 21s - loss: 0.3761 - auroc: 0.75 - ETA: 21s - loss: 0.3762 - auroc: 0.75 - ETA: 20s - loss: 0.3762 - auroc: 0.75 - ETA: 20s - loss: 0.3762 - auroc: 0.75 - ETA: 20s - loss: 0.3761 - auroc: 0.75 - ETA: 19s - loss: 0.3760 - auroc: 0.75 - ETA: 19s - loss: 0.3759 - auroc: 0.75 - ETA: 19s - loss: 0.3760 - auroc: 0.75 - ETA: 18s - loss: 0.3760 - auroc: 0.75 - ETA: 18s - loss: 0.3759 - auroc: 0.75 - ETA: 18s - loss: 0.3761 - auroc: 0.75 - ETA: 17s - loss: 0.3761 - auroc: 0.75 - ETA: 17s - loss: 0.3761 - auroc: 0.75 - ETA: 17s - loss: 0.3761 - auroc: 0.75 - ETA: 16s - loss: 0.3762 - auroc: 0.75 - ETA: 16s - loss: 0.3762 - auroc: 0.75 - ETA: 16s - loss: 0.3762 - auroc: 0.75 - ETA: 15s - loss: 0.3762 - auroc: 0.75 - ETA: 15s - loss: 0.3762 - auroc: 0.75 - ETA: 15s - loss: 0.3763 - auroc: 0.75 - ETA: 14s - loss: 0.3763 - auroc: 0.75 - ETA: 14s - loss: 0.3763 - auroc: 0.75 - ETA: 14s - loss: 0.3763 - auroc: 0.75 - ETA: 13s - loss: 0.3763 - auroc: 0.75 - ETA: 13s - loss: 0.3763 - auroc: 0.75 - ETA: 13s - loss: 0.3762 - auroc: 0.75 - ETA: 12s - loss: 0.3763 - auroc: 0.75 - ETA: 12s - loss: 0.3763 - auroc: 0.75 - ETA: 12s - loss: 0.3763 - auroc: 0.75 - ETA: 11s - loss: 0.3763 - auroc: 0.75 - ETA: 11s - loss: 0.3763 - auroc: 0.75 - ETA: 11s - loss: 0.3763 - auroc: 0.75 - ETA: 10s - loss: 0.3762 - auroc: 0.75 - ETA: 10s - loss: 0.3761 - auroc: 0.75 - ETA: 10s - loss: 0.3761 - auroc: 0.75 - ETA: 9s - loss: 0.3761 - auroc: 0.7585 - ETA: 9s - loss: 0.3761 - auroc: 0.758 - ETA: 9s - loss: 0.3760 - auroc: 0.758 - ETA: 8s - loss: 0.3761 - auroc: 0.758 - ETA: 8s - loss: 0.3761 - auroc: 0.758 - ETA: 8s - loss: 0.3760 - auroc: 0.758 - ETA: 7s - loss: 0.3759 - auroc: 0.758576473/76473 [==============================] - ETA: 7s - loss: 0.3760 - auroc: 0.758 - ETA: 7s - loss: 0.3760 - auroc: 0.758 - ETA: 6s - loss: 0.3760 - auroc: 0.758 - ETA: 6s - loss: 0.3761 - auroc: 0.758 - ETA: 6s - loss: 0.3761 - auroc: 0.758 - ETA: 5s - loss: 0.3761 - auroc: 0.758 - ETA: 5s - loss: 0.3761 - auroc: 0.758 - ETA: 5s - loss: 0.3761 - auroc: 0.758 - ETA: 4s - loss: 0.3760 - auroc: 0.758 - ETA: 4s - loss: 0.3759 - auroc: 0.758 - ETA: 4s - loss: 0.3760 - auroc: 0.758 - ETA: 3s - loss: 0.3760 - auroc: 0.758 - ETA: 3s - loss: 0.3760 - auroc: 0.758 - ETA: 3s - loss: 0.3760 - auroc: 0.758 - ETA: 2s - loss: 0.3760 - auroc: 0.758 - ETA: 2s - loss: 0.3760 - auroc: 0.758 - ETA: 2s - loss: 0.3759 - auroc: 0.758 - ETA: 1s - loss: 0.3759 - auroc: 0.758 - ETA: 1s - loss: 0.3759 - auroc: 0.758 - ETA: 1s - loss: 0.3758 - auroc: 0.759 - ETA: 0s - loss: 0.3758 - auroc: 0.759 - ETA: 0s - loss: 0.3759 - auroc: 0.759 - ETA: 0s - loss: 0.3760 - auroc: 0.758 - 449s 6ms/step - loss: 0.3760 - auroc: 0.7589 - val_loss: 0.3779 - val_auroc: 0.7626

Epoch 00001: val_auroc improved from -inf to 0.76259, saving model to model_3.h5
Epoch 2/20
12416/76473 [===>..........................] - ETA: 6:22 - loss: 0.3598 - auroc: 0.833 - ETA: 6:28 - loss: 0.4043 - auroc: 0.801 - ETA: 6:23 - loss: 0.3879 - auroc: 0.814 - ETA: 6:20 - loss: 0.3739 - auroc: 0.812 - ETA: 6:17 - loss: 0.3683 - auroc: 0.800 - ETA: 6:17 - loss: 0.3793 - auroc: 0.801 - ETA: 6:19 - loss: 0.3883 - auroc: 0.788 - ETA: 6:20 - loss: 0.3753 - auroc: 0.803 - ETA: 6:19 - loss: 0.3649 - auroc: 0.802 - ETA: 6:19 - loss: 0.3543 - auroc: 0.780 - ETA: 6:18 - loss: 0.3669 - auroc: 0.779 - ETA: 6:18 - loss: 0.3615 - auroc: 0.779 - ETA: 6:19 - loss: 0.3643 - auroc: 0.769 - ETA: 6:20 - loss: 0.3624 - auroc: 0.774 - ETA: 6:20 - loss: 0.3658 - auroc: 0.774 - ETA: 6:20 - loss: 0.3652 - auroc: 0.776 - ETA: 6:20 - loss: 0.3611 - auroc: 0.780 - ETA: 6:21 - loss: 0.3538 - auroc: 0.786 - ETA: 6:20 - loss: 0.3536 - auroc: 0.791 - ETA: 6:20 - loss: 0.3545 - auroc: 0.779 - ETA: 6:20 - loss: 0.3510 - auroc: 0.773 - ETA: 6:19 - loss: 0.3541 - auroc: 0.773 - ETA: 6:19 - loss: 0.3522 - auroc: 0.774 - ETA: 6:18 - loss: 0.3561 - auroc: 0.775 - ETA: 6:19 - loss: 0.3552 - auroc: 0.778 - ETA: 6:19 - loss: 0.3594 - auroc: 0.778 - ETA: 6:18 - loss: 0.3546 - auroc: 0.783 - ETA: 6:17 - loss: 0.3589 - auroc: 0.779 - ETA: 6:17 - loss: 0.3630 - auroc: 0.780 - ETA: 6:16 - loss: 0.3589 - auroc: 0.784 - ETA: 6:16 - loss: 0.3562 - auroc: 0.786 - ETA: 6:15 - loss: 0.3618 - auroc: 0.788 - ETA: 6:15 - loss: 0.3625 - auroc: 0.784 - ETA: 6:15 - loss: 0.3608 - auroc: 0.787 - ETA: 6:14 - loss: 0.3594 - auroc: 0.785 - ETA: 6:14 - loss: 0.3571 - auroc: 0.787 - ETA: 6:13 - loss: 0.3558 - auroc: 0.789 - ETA: 6:13 - loss: 0.3557 - auroc: 0.788 - ETA: 6:12 - loss: 0.3586 - auroc: 0.787 - ETA: 6:12 - loss: 0.3580 - auroc: 0.785 - ETA: 6:11 - loss: 0.3556 - auroc: 0.785 - ETA: 6:11 - loss: 0.3549 - auroc: 0.787 - ETA: 6:11 - loss: 0.3540 - auroc: 0.786 - ETA: 6:11 - loss: 0.3559 - auroc: 0.786 - ETA: 6:10 - loss: 0.3576 - auroc: 0.787 - ETA: 6:10 - loss: 0.3618 - auroc: 0.783 - ETA: 6:10 - loss: 0.3616 - auroc: 0.783 - 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loss: 0.3731 - auroc: 0.763 - ETA: 5s - loss: 0.3732 - auroc: 0.763 - ETA: 5s - loss: 0.3732 - auroc: 0.763 - ETA: 5s - loss: 0.3732 - auroc: 0.763 - ETA: 4s - loss: 0.3732 - auroc: 0.763 - ETA: 4s - loss: 0.3731 - auroc: 0.763 - ETA: 4s - loss: 0.3733 - auroc: 0.763 - ETA: 3s - loss: 0.3733 - auroc: 0.763 - ETA: 3s - loss: 0.3734 - auroc: 0.763 - ETA: 3s - loss: 0.3734 - auroc: 0.763 - ETA: 2s - loss: 0.3734 - auroc: 0.763 - ETA: 2s - loss: 0.3735 - auroc: 0.763 - ETA: 2s - loss: 0.3734 - auroc: 0.763 - ETA: 1s - loss: 0.3733 - auroc: 0.763 - ETA: 1s - loss: 0.3733 - auroc: 0.763 - ETA: 1s - loss: 0.3732 - auroc: 0.763 - ETA: 0s - loss: 0.3731 - auroc: 0.763 - ETA: 0s - loss: 0.3730 - auroc: 0.763 - ETA: 0s - loss: 0.3730 - auroc: 0.763 - 455s 6ms/step - loss: 0.3731 - auroc: 0.7634 - val_loss: 0.3740 - val_auroc: 0.7604

Epoch 00002: val_auroc did not improve from 0.76259
Epoch 3/20
12416/76473 [===>..........................] - ETA: 5:59 - loss: 0.2133 - auroc: 0.974 - ETA: 6:10 - loss: 0.3116 - auroc: 0.858 - ETA: 6:20 - loss: 0.3009 - auroc: 0.798 - ETA: 6:26 - loss: 0.3292 - auroc: 0.808 - ETA: 6:27 - loss: 0.3175 - auroc: 0.821 - ETA: 6:27 - loss: 0.3269 - auroc: 0.822 - ETA: 6:25 - loss: 0.3330 - auroc: 0.822 - ETA: 6:24 - loss: 0.3200 - auroc: 0.820 - ETA: 6:22 - loss: 0.3099 - auroc: 0.825 - ETA: 6:21 - loss: 0.3192 - auroc: 0.820 - ETA: 6:19 - loss: 0.3300 - auroc: 0.798 - ETA: 6:18 - loss: 0.3239 - auroc: 0.801 - ETA: 6:19 - loss: 0.3290 - auroc: 0.783 - ETA: 6:18 - loss: 0.3325 - auroc: 0.791 - ETA: 6:18 - loss: 0.3319 - auroc: 0.787 - ETA: 6:18 - loss: 0.3352 - auroc: 0.787 - ETA: 6:17 - loss: 0.3312 - auroc: 0.791 - ETA: 6:19 - loss: 0.3340 - auroc: 0.789 - ETA: 6:17 - loss: 0.3393 - auroc: 0.789 - ETA: 6:16 - loss: 0.3416 - auroc: 0.788 - ETA: 6:16 - loss: 0.3427 - auroc: 0.787 - ETA: 6:16 - loss: 0.3455 - auroc: 0.783 - ETA: 6:16 - loss: 0.3490 - auroc: 0.780 - ETA: 6:15 - loss: 0.3549 - auroc: 0.773 - ETA: 6:15 - loss: 0.3549 - auroc: 0.777 - ETA: 6:15 - loss: 0.3515 - auroc: 0.782 - ETA: 6:14 - loss: 0.3514 - auroc: 0.785 - ETA: 6:14 - loss: 0.3526 - auroc: 0.782 - ETA: 6:14 - loss: 0.3504 - auroc: 0.777 - ETA: 6:14 - loss: 0.3527 - auroc: 0.773 - ETA: 6:14 - loss: 0.3503 - auroc: 0.776 - ETA: 6:14 - loss: 0.3533 - auroc: 0.772 - ETA: 6:14 - loss: 0.3522 - auroc: 0.775 - ETA: 6:13 - loss: 0.3569 - auroc: 0.772 - ETA: 6:13 - loss: 0.3598 - auroc: 0.772 - ETA: 6:13 - loss: 0.3593 - auroc: 0.771 - ETA: 6:12 - loss: 0.3586 - auroc: 0.772 - ETA: 6:12 - loss: 0.3577 - auroc: 0.771 - ETA: 6:12 - loss: 0.3597 - auroc: 0.769 - ETA: 6:11 - loss: 0.3597 - auroc: 0.767 - ETA: 6:11 - loss: 0.3598 - auroc: 0.765 - ETA: 6:11 - loss: 0.3574 - auroc: 0.765 - ETA: 6:10 - loss: 0.3594 - auroc: 0.762 - ETA: 6:10 - loss: 0.3576 - auroc: 0.762 - ETA: 6:10 - loss: 0.3568 - auroc: 0.758 - ETA: 6:10 - loss: 0.3580 - auroc: 0.758 - ETA: 6:09 - loss: 0.3573 - auroc: 0.758 - ETA: 6:09 - loss: 0.3560 - auroc: 0.757 - ETA: 6:09 - loss: 0.3589 - auroc: 0.758 - ETA: 6:09 - loss: 0.3575 - auroc: 0.760 - ETA: 6:08 - loss: 0.3607 - auroc: 0.759 - ETA: 6:08 - loss: 0.3602 - auroc: 0.758 - ETA: 6:08 - loss: 0.3627 - auroc: 0.756 - ETA: 6:08 - loss: 0.3616 - auroc: 0.759 - ETA: 6:08 - loss: 0.3609 - auroc: 0.762 - ETA: 6:08 - loss: 0.3612 - auroc: 0.763 - ETA: 6:08 - loss: 0.3608 - auroc: 0.761 - ETA: 6:07 - loss: 0.3583 - auroc: 0.763 - ETA: 6:07 - loss: 0.3608 - auroc: 0.762 - ETA: 6:07 - loss: 0.3606 - auroc: 0.764 - ETA: 6:06 - loss: 0.3614 - auroc: 0.765 - ETA: 6:06 - loss: 0.3588 - auroc: 0.766 - ETA: 6:07 - loss: 0.3583 - auroc: 0.765 - ETA: 6:08 - loss: 0.3586 - auroc: 0.766 - ETA: 6:08 - loss: 0.3574 - auroc: 0.765 - ETA: 6:08 - loss: 0.3594 - auroc: 0.764 - ETA: 6:07 - loss: 0.3586 - auroc: 0.763 - ETA: 6:07 - loss: 0.3573 - auroc: 0.764 - ETA: 6:06 - loss: 0.3567 - auroc: 0.762 - ETA: 6:06 - loss: 0.3567 - auroc: 0.761 - ETA: 6:05 - loss: 0.3591 - auroc: 0.761 - 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ETA: 5:51 - loss: 0.3620 - auroc: 0.763 - ETA: 5:51 - loss: 0.3610 - auroc: 0.764 - ETA: 5:50 - loss: 0.3614 - auroc: 0.765 - ETA: 5:50 - loss: 0.3611 - auroc: 0.765 - ETA: 5:50 - loss: 0.3602 - auroc: 0.766 - ETA: 5:49 - loss: 0.3600 - auroc: 0.767 - ETA: 5:49 - loss: 0.3601 - auroc: 0.768 - ETA: 5:49 - loss: 0.3604 - auroc: 0.768 - ETA: 5:49 - loss: 0.3606 - auroc: 0.769 - ETA: 5:48 - loss: 0.3602 - auroc: 0.768 - ETA: 5:48 - loss: 0.3591 - auroc: 0.769 - ETA: 5:48 - loss: 0.3585 - auroc: 0.770 - ETA: 5:47 - loss: 0.3579 - auroc: 0.771 - ETA: 5:47 - loss: 0.3582 - auroc: 0.771 - ETA: 5:47 - loss: 0.3580 - auroc: 0.770 - ETA: 5:46 - loss: 0.3575 - auroc: 0.770 - ETA: 5:46 - loss: 0.3579 - auroc: 0.769 - ETA: 5:46 - loss: 0.3576 - auroc: 0.770 - ETA: 5:46 - loss: 0.3573 - auroc: 0.771 - ETA: 5:45 - loss: 0.3571 - auroc: 0.771 - ETA: 5:45 - loss: 0.3586 - auroc: 0.770 - ETA: 5:44 - loss: 0.3593 - auroc: 0.770 - ETA: 5:44 - loss: 0.3582 - auroc: 0.767 - ETA: 5:44 - loss: 0.3585 - auroc: 0.766 - ETA: 5:44 - loss: 0.3585 - auroc: 0.766 - ETA: 5:43 - loss: 0.3575 - auroc: 0.766 - ETA: 5:43 - loss: 0.3575 - auroc: 0.765 - ETA: 5:43 - loss: 0.3579 - auroc: 0.765 - ETA: 5:42 - loss: 0.3586 - auroc: 0.765 - ETA: 5:42 - loss: 0.3580 - auroc: 0.766 - ETA: 5:42 - loss: 0.3590 - auroc: 0.766 - ETA: 5:42 - loss: 0.3606 - auroc: 0.765 - ETA: 5:42 - loss: 0.3604 - auroc: 0.766 - ETA: 5:41 - loss: 0.3601 - auroc: 0.766 - ETA: 5:41 - loss: 0.3598 - auroc: 0.767 - ETA: 5:41 - loss: 0.3595 - auroc: 0.767 - ETA: 5:41 - loss: 0.3592 - auroc: 0.765 - ETA: 5:41 - loss: 0.3591 - auroc: 0.765 - ETA: 5:40 - loss: 0.3591 - auroc: 0.765 - ETA: 5:40 - loss: 0.3597 - auroc: 0.765 - ETA: 5:40 - loss: 0.3605 - auroc: 0.765 - ETA: 5:40 - loss: 0.3603 - auroc: 0.764 - ETA: 5:40 - loss: 0.3604 - auroc: 0.764 - ETA: 5:40 - loss: 0.3603 - auroc: 0.764 - ETA: 5:40 - loss: 0.3599 - auroc: 0.765 - ETA: 5:40 - loss: 0.3596 - auroc: 0.764 - ETA: 5:40 - loss: 0.3604 - auroc: 0.764 - ETA: 5:40 - loss: 0.3608 - auroc: 0.764 - ETA: 5:40 - loss: 0.3603 - auroc: 0.764 - ETA: 5:40 - loss: 0.3593 - auroc: 0.765 - ETA: 5:40 - loss: 0.3595 - auroc: 0.765 - ETA: 5:40 - loss: 0.3597 - auroc: 0.764 - ETA: 5:39 - loss: 0.3593 - auroc: 0.765 - ETA: 5:39 - loss: 0.3593 - auroc: 0.765 - ETA: 5:39 - loss: 0.3599 - auroc: 0.764 - ETA: 5:39 - loss: 0.3592 - auroc: 0.765 - ETA: 5:38 - loss: 0.3593 - auroc: 0.766 - ETA: 5:38 - loss: 0.3595 - auroc: 0.766 - ETA: 5:37 - loss: 0.3594 - auroc: 0.766 - ETA: 5:37 - loss: 0.3591 - auroc: 0.767 - ETA: 5:37 - loss: 0.3596 - auroc: 0.767 - ETA: 5:36 - loss: 0.3596 - auroc: 0.767 - ETA: 5:36 - loss: 0.3600 - auroc: 0.766 - ETA: 5:36 - loss: 0.3598 - auroc: 0.765 - ETA: 5:35 - loss: 0.3605 - auroc: 0.765 - ETA: 5:35 - loss: 0.3605 - auroc: 0.764 - ETA: 5:35 - loss: 0.3606 - auroc: 0.764 - ETA: 5:34 - loss: 0.3605 - auroc: 0.764 - ETA: 5:34 - loss: 0.3606 - auroc: 0.763 - ETA: 5:34 - loss: 0.3603 - auroc: 0.764 - ETA: 5:33 - loss: 0.3600 - auroc: 0.764 - ETA: 5:33 - loss: 0.3609 - auroc: 0.764 - ETA: 5:33 - loss: 0.3603 - auroc: 0.764 - ETA: 5:32 - loss: 0.3604 - auroc: 0.764 - ETA: 5:32 - loss: 0.3594 - auroc: 0.766024832/76473 [========>.....................] - ETA: 5:32 - loss: 0.3599 - auroc: 0.765 - ETA: 5:31 - loss: 0.3601 - auroc: 0.765 - ETA: 5:31 - loss: 0.3605 - auroc: 0.766 - ETA: 5:31 - loss: 0.3602 - auroc: 0.766 - ETA: 5:30 - loss: 0.3609 - auroc: 0.766 - ETA: 5:30 - loss: 0.3612 - auroc: 0.766 - ETA: 5:30 - loss: 0.3616 - auroc: 0.766 - ETA: 5:29 - loss: 0.3618 - auroc: 0.765 - ETA: 5:29 - loss: 0.3621 - auroc: 0.766 - ETA: 5:29 - loss: 0.3623 - auroc: 0.766 - ETA: 5:28 - loss: 0.3626 - auroc: 0.766 - ETA: 5:28 - loss: 0.3625 - auroc: 0.766 - ETA: 5:28 - loss: 0.3628 - auroc: 0.766 - ETA: 5:27 - loss: 0.3627 - auroc: 0.766 - ETA: 5:27 - loss: 0.3628 - auroc: 0.766 - ETA: 5:27 - loss: 0.3629 - auroc: 0.765 - ETA: 5:26 - loss: 0.3628 - auroc: 0.766 - ETA: 5:26 - loss: 0.3628 - auroc: 0.765 - ETA: 5:26 - loss: 0.3629 - auroc: 0.766 - ETA: 5:25 - loss: 0.3628 - auroc: 0.766 - 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auroc: 0.765 - ETA: 5s - loss: 0.3707 - auroc: 0.765 - ETA: 5s - loss: 0.3707 - auroc: 0.765 - ETA: 5s - loss: 0.3707 - auroc: 0.765 - ETA: 4s - loss: 0.3705 - auroc: 0.765 - ETA: 4s - loss: 0.3705 - auroc: 0.765 - ETA: 4s - loss: 0.3705 - auroc: 0.766 - ETA: 3s - loss: 0.3706 - auroc: 0.765 - ETA: 3s - loss: 0.3707 - auroc: 0.765 - ETA: 3s - loss: 0.3707 - auroc: 0.765 - ETA: 2s - loss: 0.3708 - auroc: 0.765 - ETA: 2s - loss: 0.3708 - auroc: 0.765 - ETA: 2s - loss: 0.3708 - auroc: 0.765 - ETA: 1s - loss: 0.3708 - auroc: 0.765 - ETA: 1s - loss: 0.3707 - auroc: 0.765 - ETA: 1s - loss: 0.3707 - auroc: 0.765 - ETA: 0s - loss: 0.3707 - auroc: 0.765 - ETA: 0s - loss: 0.3708 - auroc: 0.765 - ETA: 0s - loss: 0.3708 - auroc: 0.765 - 448s 6ms/step - loss: 0.3708 - auroc: 0.7658 - val_loss: 0.3794 - val_auroc: 0.7633

Epoch 00003: val_auroc improved from 0.76259 to 0.76327, saving model to model_3.h5
Epoch 4/20
12416/76473 [===>..........................] - ETA: 6:26 - loss: 0.2878 - auroc: 0.898 - ETA: 6:14 - loss: 0.3123 - auroc: 0.851 - ETA: 6:23 - loss: 0.3520 - auroc: 0.814 - ETA: 6:19 - loss: 0.3364 - auroc: 0.843 - ETA: 6:20 - loss: 0.3521 - auroc: 0.820 - ETA: 6:26 - loss: 0.3756 - auroc: 0.808 - ETA: 6:28 - loss: 0.3967 - auroc: 0.785 - ETA: 6:28 - loss: 0.4022 - auroc: 0.762 - ETA: 6:27 - loss: 0.3890 - auroc: 0.766 - ETA: 6:24 - loss: 0.3862 - auroc: 0.770 - ETA: 6:25 - loss: 0.3831 - auroc: 0.772 - ETA: 6:24 - loss: 0.3847 - auroc: 0.770 - ETA: 6:24 - loss: 0.3878 - auroc: 0.765 - ETA: 6:25 - loss: 0.3759 - auroc: 0.769 - ETA: 6:24 - loss: 0.3710 - auroc: 0.768 - ETA: 6:25 - loss: 0.3759 - auroc: 0.764 - ETA: 6:25 - loss: 0.3659 - auroc: 0.775 - ETA: 6:23 - loss: 0.3576 - auroc: 0.785 - ETA: 6:22 - loss: 0.3571 - auroc: 0.793 - ETA: 6:22 - loss: 0.3598 - auroc: 0.790 - ETA: 6:22 - loss: 0.3530 - auroc: 0.794 - ETA: 6:22 - loss: 0.3591 - auroc: 0.793 - ETA: 6:22 - loss: 0.3570 - auroc: 0.794 - 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ETA: 5:28 - loss: 0.3644 - auroc: 0.769 - ETA: 5:28 - loss: 0.3643 - auroc: 0.769 - ETA: 5:27 - loss: 0.3647 - auroc: 0.769324832/76473 [========>.....................] - ETA: 5:27 - loss: 0.3646 - auroc: 0.769 - ETA: 5:27 - loss: 0.3651 - auroc: 0.769 - ETA: 5:26 - loss: 0.3653 - auroc: 0.770 - ETA: 5:26 - loss: 0.3652 - auroc: 0.770 - ETA: 5:26 - loss: 0.3663 - auroc: 0.769 - ETA: 5:25 - loss: 0.3661 - auroc: 0.769 - ETA: 5:25 - loss: 0.3663 - auroc: 0.770 - ETA: 5:25 - loss: 0.3665 - auroc: 0.770 - ETA: 5:24 - loss: 0.3669 - auroc: 0.769 - ETA: 5:24 - loss: 0.3669 - auroc: 0.769 - ETA: 5:23 - loss: 0.3665 - auroc: 0.768 - ETA: 5:23 - loss: 0.3659 - auroc: 0.769 - ETA: 5:23 - loss: 0.3664 - auroc: 0.768 - ETA: 5:22 - loss: 0.3667 - auroc: 0.768 - ETA: 5:22 - loss: 0.3662 - auroc: 0.769 - ETA: 5:22 - loss: 0.3660 - auroc: 0.767 - ETA: 5:22 - loss: 0.3660 - auroc: 0.766 - ETA: 5:21 - loss: 0.3661 - auroc: 0.766 - ETA: 5:21 - loss: 0.3660 - auroc: 0.766 - ETA: 5:21 - loss: 0.3658 - auroc: 0.766 - 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Epoch 00004: val_auroc did not improve from 0.76327
Epoch 5/20
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auroc: 0.770 - ETA: 5s - loss: 0.3678 - auroc: 0.769 - ETA: 5s - loss: 0.3678 - auroc: 0.769 - ETA: 5s - loss: 0.3678 - auroc: 0.769 - ETA: 4s - loss: 0.3678 - auroc: 0.769 - ETA: 4s - loss: 0.3679 - auroc: 0.769 - ETA: 4s - loss: 0.3680 - auroc: 0.769 - ETA: 3s - loss: 0.3681 - auroc: 0.769 - ETA: 3s - loss: 0.3680 - auroc: 0.769 - ETA: 3s - loss: 0.3679 - auroc: 0.769 - ETA: 2s - loss: 0.3679 - auroc: 0.769 - ETA: 2s - loss: 0.3680 - auroc: 0.769 - ETA: 2s - loss: 0.3680 - auroc: 0.769 - ETA: 1s - loss: 0.3679 - auroc: 0.769 - ETA: 1s - loss: 0.3679 - auroc: 0.769 - ETA: 1s - loss: 0.3679 - auroc: 0.769 - ETA: 0s - loss: 0.3679 - auroc: 0.769 - ETA: 0s - loss: 0.3680 - auroc: 0.769 - ETA: 0s - loss: 0.3679 - auroc: 0.770 - 453s 6ms/step - loss: 0.3678 - auroc: 0.7700 - val_loss: 0.3756 - val_auroc: 0.7596

Epoch 00005: val_auroc did not improve from 0.76327
Epoch 6/20
12416/76473 [===>..........................] - ETA: 5:58 - loss: 0.4470 - auroc: 0.817 - ETA: 6:16 - loss: 0.4194 - auroc: 0.762 - ETA: 6:16 - loss: 0.4481 - auroc: 0.715 - ETA: 6:21 - loss: 0.3882 - auroc: 0.768 - ETA: 6:21 - loss: 0.3509 - auroc: 0.795 - ETA: 6:19 - loss: 0.3256 - auroc: 0.796 - ETA: 6:21 - loss: 0.3523 - auroc: 0.779 - ETA: 6:20 - loss: 0.3520 - auroc: 0.789 - ETA: 6:19 - loss: 0.3544 - auroc: 0.791 - ETA: 6:17 - loss: 0.3516 - auroc: 0.793 - ETA: 6:19 - loss: 0.3523 - auroc: 0.804 - ETA: 6:21 - loss: 0.3527 - auroc: 0.799 - ETA: 6:20 - loss: 0.3445 - auroc: 0.802 - ETA: 6:20 - loss: 0.3485 - auroc: 0.806 - ETA: 6:20 - loss: 0.3584 - auroc: 0.804 - ETA: 6:20 - loss: 0.3609 - auroc: 0.803 - ETA: 6:19 - loss: 0.3627 - auroc: 0.798 - ETA: 6:19 - loss: 0.3606 - auroc: 0.791 - ETA: 6:18 - loss: 0.3545 - auroc: 0.798 - ETA: 6:18 - loss: 0.3464 - auroc: 0.800 - ETA: 6:19 - loss: 0.3449 - auroc: 0.797 - ETA: 6:19 - loss: 0.3473 - auroc: 0.795 - ETA: 6:19 - loss: 0.3518 - auroc: 0.789 - 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auroc: 0.770 - ETA: 5s - loss: 0.3674 - auroc: 0.770 - ETA: 5s - loss: 0.3675 - auroc: 0.770 - ETA: 5s - loss: 0.3674 - auroc: 0.770 - ETA: 4s - loss: 0.3674 - auroc: 0.770 - ETA: 4s - loss: 0.3674 - auroc: 0.770 - ETA: 4s - loss: 0.3675 - auroc: 0.770 - ETA: 3s - loss: 0.3675 - auroc: 0.770 - ETA: 3s - loss: 0.3675 - auroc: 0.770 - ETA: 3s - loss: 0.3676 - auroc: 0.770 - ETA: 2s - loss: 0.3676 - auroc: 0.770 - ETA: 2s - loss: 0.3676 - auroc: 0.770 - ETA: 2s - loss: 0.3676 - auroc: 0.770 - ETA: 1s - loss: 0.3676 - auroc: 0.770 - ETA: 1s - loss: 0.3677 - auroc: 0.770 - ETA: 1s - loss: 0.3677 - auroc: 0.770 - ETA: 0s - loss: 0.3677 - auroc: 0.770 - ETA: 0s - loss: 0.3678 - auroc: 0.770 - ETA: 0s - loss: 0.3677 - auroc: 0.770 - 446s 6ms/step - loss: 0.3676 - auroc: 0.7705 - val_loss: 0.3738 - val_auroc: 0.7605

Epoch 00006: val_auroc did not improve from 0.76327
Restoring model weights from the end of the best epoch

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.
Epoch 00006: early stopping

AUC Score

In [7]:
history = model_3.predict(train_data_3)

print("AUC score is {}".format(roc_auc_score(Y_test,history)))

AUC score is 0.7632732608913567

Tensorboard images

In [8]:
Image(filename='Model_3_1.jpg') #auroc
Out[8]:
In [9]:
Image(filename='Model_3_2.jpg') #loss
Out[9]:
In [10]:
Image(filename='Model_3_3.jpg') #val_auroc
Out[10]:
In [12]:
Image(filename='Model_3_4.jpg') #val_loss
Out[12]:

Observation

Trained models with three architectures

Model 3 gives better performance that the other two models and its AUC is 0.763

Model 1 took more time to train

Tried various models for best results

Displayed the results with Pretty Table

In [83]:
http://zetcode.com/python/prettytable/
from prettytable import PrettyTable

x_pretty_table = PrettyTable()
x_pretty_table.field_names = ["Model","Total params","Trainable params","Non-trainable params","AUC Score"]

x_pretty_table.add_row(["Model1","1,551,551","51,543","1,500,008","0.75"])
x_pretty_table.add_row(["Model2","8,416,423","1,098,759","7,317,664","0.754"])
x_pretty_table.add_row(["Model3","1,777,974","277,974","1,500,000","0.763"])


print(x_pretty_table)

+--------+--------------+------------------+----------------------+-----------+
| Model  | Total params | Trainable params | Non-trainable params | AUC Score |
+--------+--------------+------------------+----------------------+-----------+
| Model1 |  1,551,551   |      51,543      |      1,500,008       |    0.75   |
| Model2 |  8,416,423   |    1,098,759     |      7,317,664       |   0.754   |
| Model3 |  1,777,974   |     277,974      |      1,500,000       |   0.763   |
+--------+--------------+------------------+----------------------+-----------+